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<feed xmlns="http://www.w3.org/2005/Atom"><title>Raul E. Lopez Briega</title><link href="http://relopezbriega.github.io/" rel="alternate"></link><link href="/feeds/machine-learning.atom.xml" rel="self"></link><id>http://relopezbriega.github.io/</id><updated>2016-05-29T00:00:00-03:00</updated><entry><title>Machine Learning con Python - Sobreajuste</title><link href="http://relopezbriega.github.io/blog/2016/05/29/machine-learning-con-python-sobreajuste/" rel="alternate"></link><published>2016-05-29T00:00:00-03:00</published><author><name>Raul E. Lopez Briega</name></author><id>tag:relopezbriega.github.io,2016-05-29:blog/2016/05/29/machine-learning-con-python-sobreajuste/</id><summary type="html">&lt;p&gt;
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&lt;p&gt;&lt;img alt="Machine Learning" title="Machine Learning" src="http://relopezbriega.github.io/images/machine-learning.jpg"&gt;&lt;/p&gt;

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&lt;h2 id="Introducci&amp;#243;n"&gt;Introducci&amp;#243;n&lt;a class="anchor-link" href="#Introducci&amp;#243;n"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;
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&lt;p&gt;Uno de los conceptos más importantes en &lt;a href="http://relopezbriega.github.io/tag/machine-learning.html"&gt;Machine Learning&lt;/a&gt; es el &lt;strong&gt;&lt;em&gt;&lt;a href="https://en.wikipedia.org/wiki/Overfitting"&gt;overfitting&lt;/a&gt; o &lt;a href="https://es.wikipedia.org/wiki/Sobreajuste"&gt;sobreajuste&lt;/a&gt;&lt;/em&gt;&lt;/strong&gt; del modelo. Comprender como un modelo se &lt;em&gt;ajusta&lt;/em&gt; a los datos es muy importante para entender las causas de baja precisión en las predicciones. Un modelo va a estar &lt;em&gt;&lt;a href="https://es.wikipedia.org/wiki/Sobreajuste"&gt;sobreajustado&lt;/a&gt;&lt;/em&gt; cuando vemos que se desempeña bien con los datos de entrenamiento, pero su precisión es notablemente más baja con los datos de evaluación; esto se debe a que el modelo ha memorizado los datos que ha visto y no pudo &lt;em&gt;generalizar&lt;/em&gt; las reglas para predecir los datos que no ha visto. De aquí también la importancia de siempre contar con dos &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;conjuntos de datos&lt;/a&gt; distintos, uno para entrenar el modelo y otro para evaluar su precisión; ya que si utilizamos el mismo &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt; para las dos tareas, no tendríamos forma de determinar como el modelo se comporta con datos que nunca ha visto.&lt;/p&gt;

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&lt;h2 id="&amp;#191;C&amp;#243;mo-reconocer-el-sobreajuste?"&gt;&amp;#191;C&amp;#243;mo reconocer el sobreajuste?&lt;a class="anchor-link" href="#&amp;#191;C&amp;#243;mo-reconocer-el-sobreajuste?"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;&lt;p&gt;En líneas generales el &lt;a href="https://es.wikipedia.org/wiki/Sobreajuste"&gt;sobreajuste&lt;/a&gt; va a estar relacionado con la complejidad del modelo, mientras más complejidad le agreguemos, mayor va a ser la tendencia a &lt;em&gt;&lt;a href="https://es.wikipedia.org/wiki/Sobreajuste"&gt;sobreajustarse&lt;/a&gt;&lt;/em&gt; a los datos, ya que va a contar con mayor flexibilidad para realizar las predicciones y puede ser que los patrones que encuentre estén relacionados con el &lt;em&gt;ruido&lt;/em&gt; (pequeños errores aleatorios) en los datos y no con la verdadera señal o relación subyacente.&lt;/p&gt;
&lt;p&gt;No existe una regla general para establecer cual es el nivel ideal de complejidad que le podemos otorgar a nuestro modelo sin caer en el &lt;a href="https://es.wikipedia.org/wiki/Sobreajuste"&gt;sobreajuste&lt;/a&gt;; pero podemos valernos de algunas herramientas analíticas para intentar entender como el modelo se ajusta a los datos y reconocer el &lt;a href="https://es.wikipedia.org/wiki/Sobreajuste"&gt;sobreajuste&lt;/a&gt;. Veamos un ejemplo.&lt;/p&gt;

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&lt;h3 id="&amp;#193;rboles-de-Decisi&amp;#243;n-y-sobreajuste"&gt;&amp;#193;rboles de Decisi&amp;#243;n y sobreajuste&lt;a class="anchor-link" href="#&amp;#193;rboles-de-Decisi&amp;#243;n-y-sobreajuste"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;Los &lt;a href="https://es.wikipedia.org/wiki/%C3%81rbol_de_decisi%C3%B3n"&gt;Árboles de Decisión&lt;/a&gt; pueden ser muchas veces una herramienta muy precisa, pero también con mucha tendencia al &lt;a href="https://es.wikipedia.org/wiki/Sobreajuste"&gt;sobreajuste&lt;/a&gt;. Para construir estos modelos aplicamos un procedimiento recursivo para encontrar los atributos que nos proporcionan más información sobre distintos subconjuntos de datos, cada vez más pequeños. Si aplicamos este procedimiento en forma reiterada, eventualmente podemos llegar a un árbol en el que cada &lt;em&gt;hoja&lt;/em&gt; tenga una sola instancia de nuestra variable objetivo a clasificar. En este caso extremo, el &lt;a href="https://es.wikipedia.org/wiki/%C3%81rbol_de_decisi%C3%B3n"&gt;Árbol de Decisión&lt;/a&gt; va a tener una pobre &lt;em&gt;generalización&lt;/em&gt; y estar bastante &lt;a href="https://es.wikipedia.org/wiki/Sobreajuste"&gt;sobreajustado&lt;/a&gt;; ya que cada instancia de los datos de entrenamiento va a encontrar el camino que lo lleve eventualmente a la hoja que lo contiene, alcanzando así una precisión del 100% con los datos de entrenamiento. Veamos un ejemplo sencillo con la ayuda de &lt;a href="http://python.org/"&gt;Python&lt;/a&gt;.&lt;/p&gt;

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&lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Importando las librerías que vamos a utilizar&lt;/span&gt;
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;pandas&lt;/span&gt; &lt;span class="kn"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;pd&lt;/span&gt;
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;numpy&lt;/span&gt; &lt;span class="kn"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;np&lt;/span&gt; 
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;matplotlib.pyplot&lt;/span&gt; &lt;span class="kn"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;plt&lt;/span&gt; 
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;seaborn&lt;/span&gt; &lt;span class="kn"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;sns&lt;/span&gt; 
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;sklearn.cross_validation&lt;/span&gt; &lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="n"&gt;train_test_split&lt;/span&gt;
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;sklearn.datasets&lt;/span&gt; &lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="n"&gt;make_classification&lt;/span&gt;
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;sklearn.svm&lt;/span&gt; &lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="n"&gt;SVC&lt;/span&gt;
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;sklearn.tree&lt;/span&gt; &lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="n"&gt;DecisionTreeClassifier&lt;/span&gt;
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;random&lt;/span&gt;&lt;span class="p"&gt;;&lt;/span&gt; &lt;span class="n"&gt;random&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;seed&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mi"&gt;1982&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="c1"&gt;# graficos incrustados&lt;/span&gt;
&lt;span class="o"&gt;%&lt;/span&gt;&lt;span class="k"&gt;matplotlib&lt;/span&gt; inline

&lt;span class="c1"&gt;# parametros esteticos de seaborn&lt;/span&gt;
&lt;span class="n"&gt;sns&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_palette&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;deep&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;desat&lt;/span&gt;&lt;span class="o"&gt;=.&lt;/span&gt;&lt;span class="mi"&gt;6&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;sns&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_context&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;rc&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="p"&gt;{&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;figure.figsize&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt; &lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mi"&gt;8&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;4&lt;/span&gt;&lt;span class="p"&gt;)})&lt;/span&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Ejemplo en python - árboles de decisión&lt;/span&gt;
&lt;span class="c1"&gt;# dummy data con 100 atributos y 2 clases&lt;/span&gt;
&lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;make_classification&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mi"&gt;10000&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;100&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;n_informative&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;3&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;n_classes&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;2&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                          &lt;span class="n"&gt;random_state&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;1982&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="c1"&gt;# separ los datos en train y eval&lt;/span&gt;
&lt;span class="n"&gt;x_train&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;x_eval&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y_train&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y_eval&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;train_test_split&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;test_size&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mf"&gt;0.35&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
                                                    &lt;span class="n"&gt;train_size&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mf"&gt;0.65&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                                                    &lt;span class="n"&gt;random_state&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;1982&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="c1"&gt;# creando el modelo sin control de profundidad, va a continuar hasta&lt;/span&gt;
&lt;span class="c1"&gt;# que todas las hojas sean puras&lt;/span&gt;
&lt;span class="n"&gt;arbol&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;DecisionTreeClassifier&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;criterion&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;entropy&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="c1"&gt;# Ajustando el modelo&lt;/span&gt;
&lt;span class="n"&gt;arbol&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;fit&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;x_train&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y_train&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
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&lt;pre class="ipynb"&gt;DecisionTreeClassifier(class_weight=None, criterion=&amp;#39;entropy&amp;#39;, max_depth=None,
            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            presort=False, random_state=None, splitter=&amp;#39;best&amp;#39;)&lt;/pre&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# precisión del modelo en datos de entrenamiento.&lt;/span&gt;
&lt;span class="nb"&gt;print&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;precisión entranamiento: &lt;/span&gt;&lt;span class="si"&gt;{0: .2f}&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;format&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;
        &lt;span class="n"&gt;arbol&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;score&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;x_train&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y_train&lt;/span&gt;&lt;span class="p"&gt;)))&lt;/span&gt;
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&lt;pre class="ipynb"&gt;precisión entranamiento:  1.00
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&lt;p&gt;Logramos una precisión del 100 %, increíble, este modelo no se equivoca! deberíamos utilizarlo para jugar a la lotería y ver si ganamos algunos millones; o tal vez, no?. Veamos como se comporta con los datos de evaluación.&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# precisión del modelo en datos de evaluación.&lt;/span&gt;
&lt;span class="nb"&gt;print&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;precisión evaluación: &lt;/span&gt;&lt;span class="si"&gt;{0: .2f}&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;format&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;
        &lt;span class="n"&gt;arbol&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;score&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;x_eval&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y_eval&lt;/span&gt;&lt;span class="p"&gt;)))&lt;/span&gt;
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&lt;pre class="ipynb"&gt;precisión evaluación:  0.87
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&lt;p&gt;Ah, ahora nuestro modelo ya no se muestra tan preciso, esto se debe a que seguramente esta &lt;a href="https://es.wikipedia.org/wiki/Sobreajuste"&gt;sobreajustado&lt;/a&gt;, ya que dejamos crecer el árbol hasta que cada hoja estuviera pura (es decir que solo contenga datos de una sola de las clases a predecir). Una alternativa para reducir el &lt;a href="https://es.wikipedia.org/wiki/Sobreajuste"&gt;sobreajuste&lt;/a&gt; y ver si podemos lograr que &lt;em&gt;generalice&lt;/em&gt; mejor y por tanto tenga más precisión para datos nunca vistos, es tratar de reducir la complejidad del modelo por medio de controlar la profundidad que puede alcanzar el &lt;a href="https://es.wikipedia.org/wiki/%C3%81rbol_de_decisi%C3%B3n"&gt;Árbol de Decisión&lt;/a&gt;.&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# profundidad del arbol de decisión.&lt;/span&gt;
&lt;span class="n"&gt;arbol&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;tree_&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;max_depth&lt;/span&gt;
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&lt;pre class="ipynb"&gt;22&lt;/pre&gt;
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&lt;p&gt;Este caso nuestro modelo tiene una profundidad de 22 nodos; veamos si reduciendo esa cantidad podemos mejorar la precisión en los datos de evaluación. Por ejemplo, pongamos un máximo de profundidad de tan solo 5 nodos.&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# modelo dos, con control de profundiad de 5 nodos&lt;/span&gt;
&lt;span class="n"&gt;arbol2&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;DecisionTreeClassifier&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;criterion&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;entropy&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;max_depth&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;5&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="c1"&gt;# Ajustando el modelo&lt;/span&gt;
&lt;span class="n"&gt;arbol2&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;fit&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;x_train&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y_train&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
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&lt;pre class="ipynb"&gt;DecisionTreeClassifier(class_weight=None, criterion=&amp;#39;entropy&amp;#39;, max_depth=5,
            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            presort=False, random_state=None, splitter=&amp;#39;best&amp;#39;)&lt;/pre&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# precisión del modelo en datos de entrenamiento.&lt;/span&gt;
&lt;span class="nb"&gt;print&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;precisión entranamiento: &lt;/span&gt;&lt;span class="si"&gt;{0: .2f}&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;format&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;
        &lt;span class="n"&gt;arbol2&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;score&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;x_train&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y_train&lt;/span&gt;&lt;span class="p"&gt;)))&lt;/span&gt;
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&lt;pre class="ipynb"&gt;precisión entranamiento:  0.92
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&lt;p&gt;Ahora podemos ver que ya no tenemos un modelo con 100% de precisión en los datos de entrenamiento, sino que la precisión es bastante inferior, 92%, sin embargo si ahora medimos la precisión con los datos de evaluación vemos que la precisión es del 90%, 3 puntos por arriba de lo que habíamos conseguido con el primer modelo que nunca se equivocaba en los datos de entrenamiento.&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# precisión del modelo en datos de evaluación.&lt;/span&gt;
&lt;span class="nb"&gt;print&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;precisión evaluación: &lt;/span&gt;&lt;span class="si"&gt;{0: .2f}&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;format&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;
        &lt;span class="n"&gt;arbol2&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;score&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;x_eval&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y_eval&lt;/span&gt;&lt;span class="p"&gt;)))&lt;/span&gt;
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&lt;pre class="ipynb"&gt;precisión evaluación:  0.90
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&lt;p&gt;Esta diferencia se debe a que reducimos la complejidad del modelo para intentar ganar en generalización. También debemos tener en cuenta que si seguimos reduciendo la complejidad, podemos crear un modelo demasiado simple que en vez de estar &lt;a href="https://es.wikipedia.org/wiki/Sobreajuste"&gt;sobreajustado&lt;/a&gt; puede tener un desempeño muy por debajo del que podría tener; podríamos decir que el modelo estaría &lt;em&gt;infraajustado&lt;/em&gt; y tendría un alto nivel de &lt;em&gt;sesgo&lt;/em&gt;. Para ayudarnos a encontrar el término medio entre la complejidad del modelo y su &lt;em&gt;ajuste&lt;/em&gt; a los datos, podemos ayudarnos de herramientas gráficas. Por ejemplo podríamos crear diferentes modelos, con distintos grados de complejidad y luego graficar la precisión en función de la complejidad.&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Grafico de ajuste del árbol de decisión&lt;/span&gt;
&lt;span class="n"&gt;train_prec&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt;  &lt;span class="p"&gt;[]&lt;/span&gt;
&lt;span class="n"&gt;eval_prec&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="p"&gt;[]&lt;/span&gt;
&lt;span class="n"&gt;max_deep_list&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="nb"&gt;list&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="nb"&gt;range&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mi"&gt;3&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;23&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt;

&lt;span class="k"&gt;for&lt;/span&gt; &lt;span class="n"&gt;deep&lt;/span&gt; &lt;span class="ow"&gt;in&lt;/span&gt; &lt;span class="n"&gt;max_deep_list&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt;
    &lt;span class="n"&gt;arbol3&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;DecisionTreeClassifier&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;criterion&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;entropy&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;max_depth&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;deep&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
    &lt;span class="n"&gt;arbol3&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;fit&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;x_train&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y_train&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
    &lt;span class="n"&gt;train_prec&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;append&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;arbol3&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;score&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;x_train&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y_train&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt;
    &lt;span class="n"&gt;eval_prec&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;append&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;arbol3&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;score&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;x_eval&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y_eval&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt;

&lt;span class="c1"&gt;# graficar los resultados.&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;plot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;max_deep_list&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;train_prec&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;color&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;r&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;label&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;entrenamiento&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;plot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;max_deep_list&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;eval_prec&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;color&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;b&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;label&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;evaluacion&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;title&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Grafico de ajuste arbol de decision&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;legend&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;ylabel&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;precision&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;xlabel&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;cant de nodos&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;show&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
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&lt;p&gt;El gráfico que acabamos de construir se llama &lt;em&gt;gráfico de ajuste&lt;/em&gt; y muestra la precisión del modelo en función de su complejidad. En nuestro ejemplo, podemos ver que el punto con mayor precisión, en los datos de evaluación, lo obtenemos con un nivel de profundidad de aproximadamente 5 nodos; a partir de allí el modelo pierde en &lt;em&gt;generalización&lt;/em&gt; y comienza a estar &lt;a href="https://es.wikipedia.org/wiki/Sobreajuste"&gt;sobreajustado&lt;/a&gt;. También podemos crear un gráfico similar con la ayuda de &lt;a href="http://scikit-learn.org/stable/"&gt;Scikit-learn&lt;/a&gt;, utilizando &lt;code&gt;validation_curve&lt;/code&gt;.&lt;/p&gt;

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&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[11]:&lt;/div&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# utilizando validation curve de sklearn&lt;/span&gt;
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;sklearn.learning_curve&lt;/span&gt; &lt;span class="k"&gt;import&lt;/span&gt; &lt;span class="n"&gt;validation_curve&lt;/span&gt;

&lt;span class="n"&gt;train_prec&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;eval_prec&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;validation_curve&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;estimator&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;arbol&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;x_train&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                                        &lt;span class="n"&gt;y&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;y_train&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;param_name&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;max_depth&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                                        &lt;span class="n"&gt;param_range&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;max_deep_list&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;cv&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;5&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="n"&gt;train_mean&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;mean&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;train_prec&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;axis&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;train_std&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;std&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;train_prec&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;axis&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;test_mean&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;mean&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;eval_prec&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;axis&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;test_std&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;std&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;eval_prec&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;axis&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# graficando las curvas&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;plot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;max_deep_list&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;train_mean&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;color&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;r&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;marker&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;o&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;markersize&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;5&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
         &lt;span class="n"&gt;label&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;entrenamiento&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;fill_between&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;max_deep_list&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;train_mean&lt;/span&gt; &lt;span class="o"&gt;+&lt;/span&gt; &lt;span class="n"&gt;train_std&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
                 &lt;span class="n"&gt;train_mean&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt; &lt;span class="n"&gt;train_std&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;alpha&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mf"&gt;0.15&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;color&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;r&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;plot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;max_deep_list&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;test_mean&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;color&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;b&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;linestyle&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;--&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
         &lt;span class="n"&gt;marker&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;s&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;markersize&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;5&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;label&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;evaluacion&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;fill_between&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;max_deep_list&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;test_mean&lt;/span&gt; &lt;span class="o"&gt;+&lt;/span&gt; &lt;span class="n"&gt;test_std&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
                 &lt;span class="n"&gt;test_mean&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt; &lt;span class="n"&gt;test_std&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;alpha&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mf"&gt;0.15&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;color&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;b&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;grid&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;legend&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;loc&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;center right&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;xlabel&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Cant de nodos&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;ylabel&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Precision&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;show&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
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&lt;p&gt;En este gráfico, también podemos ver que nuestro modelo tiene bastante &lt;em&gt;&lt;a href="https://es.wikipedia.org/wiki/Varianza"&gt;varianza&lt;/a&gt;&lt;/em&gt;, representada por el área esfumada.&lt;/p&gt;
&lt;h2 id="M&amp;#233;todos-para-reducir-el-Sobreajuste"&gt;M&amp;#233;todos para reducir el Sobreajuste&lt;a class="anchor-link" href="#M&amp;#233;todos-para-reducir-el-Sobreajuste"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;&lt;p&gt;Algunas de las técnicas que podemos utilizar para reducir el &lt;a href="https://es.wikipedia.org/wiki/Sobreajuste"&gt;Sobreajuste&lt;/a&gt;, son:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Utilizar &lt;em&gt;&lt;a href="https://es.wikipedia.org/wiki/Validaci%C3%B3n_cruzada"&gt;validación cruzada&lt;/a&gt;&lt;/em&gt;.&lt;/li&gt;
&lt;li&gt;Recolectar más datos.&lt;/li&gt;
&lt;li&gt;Introducir una penalización a la complejidad con alguna técnica de regularización.&lt;/li&gt;
&lt;li&gt;Optimizar los parámetros del modelo con &lt;em&gt;grid search&lt;/em&gt;.&lt;/li&gt;
&lt;li&gt;Reducir la dimensión de los datos.&lt;/li&gt;
&lt;li&gt;Aplicar técnicas de &lt;a href="http://relopezbriega.github.io/blog/2016/04/15/ejemplo-de-machine-learning-con-python-seleccion-de-atributos/"&gt;selección de atributos&lt;/a&gt;.&lt;/li&gt;
&lt;li&gt;Utilizar modelos &lt;em&gt;ensamblados&lt;/em&gt;.&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;Veamos algunos ejemplos.&lt;/p&gt;

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&lt;h3 id="Validaci&amp;#243;n-cruzada"&gt;Validaci&amp;#243;n cruzada&lt;a class="anchor-link" href="#Validaci&amp;#243;n-cruzada"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;La &lt;em&gt;&lt;a href="https://es.wikipedia.org/wiki/Validaci%C3%B3n_cruzada"&gt;validación cruzada&lt;/a&gt;&lt;/em&gt; se inicia mediante el fraccionamiento de un &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;conjunto de datos&lt;/a&gt; en un número $k$ de particiones (generalmente entre 5 y 10) llamadas &lt;em&gt;pliegues&lt;/em&gt;. La &lt;em&gt;&lt;a href="https://es.wikipedia.org/wiki/Validaci%C3%B3n_cruzada"&gt;validación cruzada&lt;/a&gt;&lt;/em&gt; luego itera entre los datos de &lt;em&gt;evaluación&lt;/em&gt; y &lt;em&gt;entrenamiento&lt;/em&gt; $k$ veces, de un modo particular. En cada iteración de la &lt;em&gt;&lt;a href="https://es.wikipedia.org/wiki/Validaci%C3%B3n_cruzada"&gt;validación cruzada&lt;/a&gt;&lt;/em&gt;, un &lt;em&gt;pliegue&lt;/em&gt; diferente se elige como los datos de &lt;em&gt;evaluación&lt;/em&gt;. En esta iteración, los otros &lt;em&gt;pliegues&lt;/em&gt; $k-1$ se combinan para formar los datos de &lt;em&gt;entrenamiento&lt;/em&gt;. Por lo tanto, en cada iteración tenemos $(k-1) / k$ de los datos utilizados para el &lt;em&gt;entrenamiento&lt;/em&gt; y $1 / k$ utilizado para la &lt;em&gt;evaluación&lt;/em&gt;.
Cada iteración produce un modelo, y por lo tanto una estimación del rendimiento de la &lt;em&gt;generalización&lt;/em&gt;, por ejemplo, una estimación de la precisión. Una vez finalizada la &lt;em&gt;&lt;a href="https://es.wikipedia.org/wiki/Validaci%C3%B3n_cruzada"&gt;validación cruzada&lt;/a&gt;&lt;/em&gt;, todos los ejemplos se han utilizado sólo una vez para &lt;em&gt;evaluar&lt;/em&gt; pero $k -1$ veces para &lt;em&gt;entrenar&lt;/em&gt;. En este punto tenemos estimaciones de rendimiento de todos los &lt;em&gt;pliegues&lt;/em&gt; y podemos calcular la &lt;a href="https://es.wikipedia.org/wiki/Media_aritm%C3%A9tica"&gt;media&lt;/a&gt; y la &lt;a href="https://es.wikipedia.org/wiki/Desviaci%C3%B3n_t%C3%ADpica"&gt;desviación estándar&lt;/a&gt; de la precisión del modelo. Veamos un ejemplo&lt;/p&gt;
&lt;p&gt;&lt;img alt="Validacion cruzada" title="Validacion cruzada" src="http://relopezbriega.github.io/images/validacion_cruzada.png"&gt;&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Ejemplo cross-validation&lt;/span&gt;
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;sklearn&lt;/span&gt; &lt;span class="k"&gt;import&lt;/span&gt; &lt;span class="n"&gt;cross_validation&lt;/span&gt;

&lt;span class="c1"&gt;# creando pliegues&lt;/span&gt;
&lt;span class="n"&gt;kpliegues&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;cross_validation&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;StratifiedKFold&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;y&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;y_train&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;n_folds&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;10&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                                        &lt;span class="n"&gt;random_state&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;2016&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="c1"&gt;# iterando entre los plieges&lt;/span&gt;
&lt;span class="n"&gt;precision&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="p"&gt;[]&lt;/span&gt;
&lt;span class="k"&gt;for&lt;/span&gt; &lt;span class="n"&gt;k&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;train&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;test&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="ow"&gt;in&lt;/span&gt; &lt;span class="nb"&gt;enumerate&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;kpliegues&lt;/span&gt;&lt;span class="p"&gt;):&lt;/span&gt;
    &lt;span class="n"&gt;arbol2&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;fit&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;x_train&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;train&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="n"&gt;y_train&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;train&lt;/span&gt;&lt;span class="p"&gt;])&lt;/span&gt; 
    &lt;span class="n"&gt;score&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;arbol2&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;score&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;x_train&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;test&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="n"&gt;y_train&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;test&lt;/span&gt;&lt;span class="p"&gt;])&lt;/span&gt;
    &lt;span class="n"&gt;precision&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;append&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;score&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
    &lt;span class="nb"&gt;print&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Pliegue: &lt;/span&gt;&lt;span class="si"&gt;{0:}&lt;/span&gt;&lt;span class="s1"&gt;, Dist Clase: &lt;/span&gt;&lt;span class="si"&gt;{1:}&lt;/span&gt;&lt;span class="s1"&gt;, Prec: &lt;/span&gt;&lt;span class="si"&gt;{2:.3f}&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;format&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;k&lt;/span&gt;&lt;span class="o"&gt;+&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                        &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;bincount&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;y_train&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;train&lt;/span&gt;&lt;span class="p"&gt;]),&lt;/span&gt; &lt;span class="n"&gt;score&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt;

&lt;span class="c1"&gt;# imprimir promedio y desvio estandar&lt;/span&gt;
&lt;span class="nb"&gt;print&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Precision promedio: &lt;/span&gt;&lt;span class="si"&gt;{0: .3f}&lt;/span&gt;&lt;span class="s1"&gt; +/- &lt;/span&gt;&lt;span class="si"&gt;{1: .3f}&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;format&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;mean&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;precision&lt;/span&gt;&lt;span class="p"&gt;),&lt;/span&gt;
                                          &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;std&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;precision&lt;/span&gt;&lt;span class="p"&gt;)))&lt;/span&gt;
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&lt;pre class="ipynb"&gt;Pliegue: 1, Dist Clase: [2918 2931], Prec: 0.909
Pliegue: 2, Dist Clase: [2918 2931], Prec: 0.896
Pliegue: 3, Dist Clase: [2918 2931], Prec: 0.897
Pliegue: 4, Dist Clase: [2919 2931], Prec: 0.920
Pliegue: 5, Dist Clase: [2919 2931], Prec: 0.895
Pliegue: 6, Dist Clase: [2919 2931], Prec: 0.912
Pliegue: 7, Dist Clase: [2919 2931], Prec: 0.871
Pliegue: 8, Dist Clase: [2919 2932], Prec: 0.906
Pliegue: 9, Dist Clase: [2919 2932], Prec: 0.884
Pliegue: 10, Dist Clase: [2919 2932], Prec: 0.891
Precision promedio:  0.898 +/-  0.014
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&lt;p&gt;En este ejemplo, utilizamos el &lt;a href="https://es.wikipedia.org/wiki/Iterador_(patr%C3%B3n_de_dise%C3%B1o)"&gt;iterador&lt;/a&gt; &lt;code&gt;StratifiedKFold&lt;/code&gt; que nos proporciona &lt;a href="http://scikit-learn.org/stable/"&gt;Scikit-learn&lt;/a&gt;. Este &lt;a href="https://es.wikipedia.org/wiki/Iterador_(patr%C3%B3n_de_dise%C3%B1o)"&gt;iterador&lt;/a&gt; es una versión mejorada de la &lt;em&gt;&lt;a href="https://es.wikipedia.org/wiki/Validaci%C3%B3n_cruzada"&gt;validación cruzada&lt;/a&gt;&lt;/em&gt;, ya que cada &lt;em&gt;pliegue&lt;/em&gt; va a estar estratificado para mantener las proporciones entre las &lt;em&gt;clases&lt;/em&gt; del &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;conjunto de datos&lt;/a&gt; original, lo que suele dar mejores estimaciones del sesgo y la varianza del modelo. También podríamos utilizar &lt;code&gt;cross_val_score&lt;/code&gt; que ya nos proporciona los resultados de la precisión que tuvo el modelo en cada &lt;em&gt;pliegue&lt;/em&gt;.&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Ejemplo con cross_val_score&lt;/span&gt;
&lt;span class="n"&gt;precision&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;cross_validation&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;cross_val_score&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;estimator&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;arbol2&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                                            &lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;x_train&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;y_train&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                                            &lt;span class="n"&gt;cv&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;10&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;n_jobs&lt;/span&gt;&lt;span class="o"&gt;=-&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="nb"&gt;print&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;precisiones: &lt;/span&gt;&lt;span class="si"&gt;{}&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;format&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;precision&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt;
&lt;span class="nb"&gt;print&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Precision promedio: &lt;/span&gt;&lt;span class="si"&gt;{0: .3f}&lt;/span&gt;&lt;span class="s1"&gt; +/- &lt;/span&gt;&lt;span class="si"&gt;{1: .3f}&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;format&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;mean&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;precision&lt;/span&gt;&lt;span class="p"&gt;),&lt;/span&gt;
                                          &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;std&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;precision&lt;/span&gt;&lt;span class="p"&gt;)))&lt;/span&gt;
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&lt;pre class="ipynb"&gt;precisiones: [ 0.906298    0.89708141  0.89708141  0.91846154  0.89538462  0.91230769
  0.87076923  0.90755008  0.8844376   0.89060092]
Precision promedio:  0.898 +/-  0.013
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&lt;h3 id="M&amp;#225;s-datos-y-curvas-de-aprendizaje"&gt;M&amp;#225;s datos y curvas de aprendizaje&lt;a class="anchor-link" href="#M&amp;#225;s-datos-y-curvas-de-aprendizaje"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;Muchas veces, reducir el &lt;a href="https://es.wikipedia.org/wiki/Sobreajuste"&gt;Sobreajuste&lt;/a&gt; es tan fácil como conseguir más datos, dame más datos y te predeciré el futuro!. Aunque en la vida real nunca es una tarea tan sencilla conseguir más datos. Otra herramienta analítica que nos ayuda a entender como reducimos el &lt;a href="https://es.wikipedia.org/wiki/Sobreajuste"&gt;Sobreajuste&lt;/a&gt; con la ayuda de más datos, son las &lt;em&gt;curvas de aprendizaje&lt;/em&gt;, las cuales grafican la precisión en función del tamaño de los datos de entrenamiento. Veamos como podemos graficarlas con la ayuda de &lt;a href="http://python.org/"&gt;Python&lt;/a&gt;.&lt;/p&gt;
&lt;p&gt;&lt;img alt="Curva de aprendizaje" title="Curva de aprendizaje" src="http://relopezbriega.github.io/images/curva_aprendizaje.png" width="600px" height="600px" &gt;&lt;/p&gt;

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&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[15]:&lt;/div&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Ejemplo Curvas de aprendizaje&lt;/span&gt;
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;sklearn.learning_curve&lt;/span&gt; &lt;span class="k"&gt;import&lt;/span&gt; &lt;span class="n"&gt;learning_curve&lt;/span&gt;

&lt;span class="n"&gt;train_sizes&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;train_scores&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;test_scores&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;learning_curve&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;estimator&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;arbol2&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                        &lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;x_train&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;y_train&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
                        &lt;span class="n"&gt;train_sizes&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;linspace&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mf"&gt;0.1&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mf"&gt;1.0&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;10&lt;/span&gt;&lt;span class="p"&gt;),&lt;/span&gt; &lt;span class="n"&gt;cv&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;10&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                        &lt;span class="n"&gt;n_jobs&lt;/span&gt;&lt;span class="o"&gt;=-&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="n"&gt;train_mean&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;mean&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;train_scores&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;axis&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;train_std&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;std&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;train_scores&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;axis&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;test_mean&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;mean&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;test_scores&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;axis&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;test_std&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;std&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;test_scores&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;axis&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# graficando las curvas&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;plot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;train_sizes&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;train_mean&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;color&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;r&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;marker&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;o&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;markersize&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;5&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
         &lt;span class="n"&gt;label&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;entrenamiento&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;fill_between&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;train_sizes&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;train_mean&lt;/span&gt; &lt;span class="o"&gt;+&lt;/span&gt; &lt;span class="n"&gt;train_std&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
                 &lt;span class="n"&gt;train_mean&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt; &lt;span class="n"&gt;train_std&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;alpha&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mf"&gt;0.15&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;color&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;r&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;plot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;train_sizes&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;test_mean&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;color&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;b&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;linestyle&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;--&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
         &lt;span class="n"&gt;marker&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;s&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;markersize&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;5&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;label&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;evaluacion&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;fill_between&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;train_sizes&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;test_mean&lt;/span&gt; &lt;span class="o"&gt;+&lt;/span&gt; &lt;span class="n"&gt;test_std&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
                 &lt;span class="n"&gt;test_mean&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt; &lt;span class="n"&gt;test_std&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;alpha&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mf"&gt;0.15&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;color&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;b&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;grid&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;title&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Curva de aprendizaje&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;legend&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;loc&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;upper right&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;xlabel&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Cant de ejemplos de entrenamiento&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;ylabel&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Precision&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;show&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
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&lt;p&gt;En este gráfico podemos ver claramente como con pocos datos la precisión entre los datos de entrenamiento y los de evaluación son muy distintas y luego a medida que la cantidad de datos va aumentando, el modelo puede generalizar mucho mejor y las precisiones se comienzan a emparejar. Este gráfico también puede ser importante a la hora de decidir invertir en la obtención de más datos, ya que por ejemplo nos indica que a partir las 2500 muestras, el modelo ya no gana mucha más precisión a pesar de obtener más datos.&lt;/p&gt;
&lt;h3 id="Optimizaci&amp;#243;n-de-par&amp;#225;metros-con-Grid-Search"&gt;Optimizaci&amp;#243;n de par&amp;#225;metros con Grid Search&lt;a class="anchor-link" href="#Optimizaci&amp;#243;n-de-par&amp;#225;metros-con-Grid-Search"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;La mayoría de los modelos de &lt;a href="http://relopezbriega.github.io/tag/machine-learning.html"&gt;Machine Learning&lt;/a&gt; cuentan con varios parámetros para ajustar su comportamiento, por lo tanto otra alternativa que tenemos para reducir el &lt;a href="https://es.wikipedia.org/wiki/Sobreajuste"&gt;Sobreajuste&lt;/a&gt; es optimizar estos parámetros por medio de un proceso conocido como &lt;em&gt;grid search&lt;/em&gt; e intentar encontrar la combinación ideal que nos proporcione mayor precisión. El enfoque que utiliza &lt;em&gt;grid search&lt;/em&gt; es bastante simple, se trata de una búsqueda exhaustiva por el paradigma de fuerza bruta en el que se especifica una lista de valores para diferentes parámetros, y la computadora evalúa el rendimiento del modelo para cada combinación de éstos parámetros para obtener el conjunto óptimo que nos brinda el mayor rendimiento.&lt;/p&gt;
&lt;p&gt;Veamos un ejemplo utilizando un modelo de &lt;a href="https://es.wikipedia.org/wiki/M%C3%A1quinas_de_vectores_de_soporte"&gt;SVM o Máquinas de vectores de soporte&lt;/a&gt;, la idea va a ser optimizar los parámetros &lt;code&gt;gamma&lt;/code&gt; y &lt;code&gt;C&lt;/code&gt; de este modelo. El parámetro &lt;code&gt;gamma&lt;/code&gt; define cuan lejos llega la influencia de un solo ejemplo de entrenamiento, con valores bajos que significan "lejos" y los valores altos significan "cerca". El parámetro &lt;code&gt;C&lt;/code&gt; es el que establece la penalización por error en la clasificación un valor bajo de este parámetro hace que la superficie de decisión sea más lisa, mientras que un valor alto tiene como objetivo que todos los ejemplos se clasifiquen correctamente, dándole más libertad al modelo para elegir más ejemplos como vectores de soporte. Tengan en cuenta que como todo proceso por fuerza bruta, puede tomar bastante tiempo según la cantidad de parámetros que utilicemos para la optimización.&lt;/p&gt;

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&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[17]:&lt;/div&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Ejemplo de grid search con SVM.&lt;/span&gt;
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;sklearn.grid_search&lt;/span&gt; &lt;span class="k"&gt;import&lt;/span&gt; &lt;span class="n"&gt;GridSearchCV&lt;/span&gt;

&lt;span class="c1"&gt;# creación del modelo&lt;/span&gt;
&lt;span class="n"&gt;svm&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;SVC&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;random_state&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;1982&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="c1"&gt;# rango de parametros&lt;/span&gt;
&lt;span class="n"&gt;rango_C&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;logspace&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="mi"&gt;2&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;10&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;10&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;rango_gamma&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;logspace&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="mi"&gt;9&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;3&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;10&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;param_grid&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="nb"&gt;dict&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;gamma&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;rango_gamma&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;C&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;rango_C&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="c1"&gt;# crear grid search&lt;/span&gt;
&lt;span class="n"&gt;gs&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;GridSearchCV&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;estimator&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;svm&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;param_grid&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;param_grid&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;scoring&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;accuracy&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                  &lt;span class="n"&gt;cv&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;5&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;&lt;span class="n"&gt;n_jobs&lt;/span&gt;&lt;span class="o"&gt;=-&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="c1"&gt;# comenzar el ajuste&lt;/span&gt;
&lt;span class="n"&gt;gs&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;gs&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;fit&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;x_train&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y_train&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# imprimir resultados&lt;/span&gt;
&lt;span class="nb"&gt;print&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;gs&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;best_score_&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="nb"&gt;print&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;gs&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;best_params_&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
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&lt;pre class="ipynb"&gt;0.870461538462
{&amp;#39;C&amp;#39;: 4.6415888336127775, &amp;#39;gamma&amp;#39;: 0.0046415888336127729}
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# utilizando el mejor modelo&lt;/span&gt;
&lt;span class="n"&gt;mejor_modelo&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;gs&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;best_estimator_&lt;/span&gt;
&lt;span class="n"&gt;mejor_modelo&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;fit&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;x_train&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y_train&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="nb"&gt;print&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Precisión: &lt;/span&gt;&lt;span class="si"&gt;{0:.3f}&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;format&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;mejor_modelo&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;score&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;x_eval&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y_eval&lt;/span&gt;&lt;span class="p"&gt;)))&lt;/span&gt;
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&lt;pre class="ipynb"&gt;Precisión: 0.864
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&lt;p&gt;En este ejemplo, primero utilizamos el objeto &lt;code&gt;GridSearchCV&lt;/code&gt; que nos permite realizar &lt;em&gt;grid search&lt;/em&gt; junto con &lt;em&gt;&lt;a href="https://es.wikipedia.org/wiki/Validaci%C3%B3n_cruzada"&gt;validación cruzada&lt;/a&gt;&lt;/em&gt;, luego comenzamos a ajustar el modelo con las diferentes combinaciones de los valores de los parámetros &lt;code&gt;gamma&lt;/code&gt; y &lt;code&gt;C&lt;/code&gt;. Finalmente imprimimos el mejor resultado de precisión y los valores de los parámetros que utilizamos para obtenerlos; por último utilizamos este mejor modelo para realizar las predicciones con los datos de &lt;em&gt;evaluación&lt;/em&gt;. Podemos ver que la precisión que obtuvimos con los datos de evaluación es casi idéntica a la que nos indicó &lt;em&gt;grid search&lt;/em&gt;, lo que indica que el modelo &lt;em&gt;generaliza&lt;/em&gt; muy bien.&lt;/p&gt;
&lt;p&gt;Aquí termina este artículo, sobre la &lt;a href="http://relopezbriega.github.io/blog/2016/04/15/ejemplo-de-machine-learning-con-python-seleccion-de-atributos/"&gt;selección de atributos&lt;/a&gt;, pueden visitar el artículo que dedique a ese tema en este &lt;a href="http://relopezbriega.github.io/blog/2016/04/15/ejemplo-de-machine-learning-con-python-seleccion-de-atributos/"&gt;link&lt;/a&gt;; en cuando a modelos ensamblados y reducción de dimensiones de los datos, espero escribir sobre esos temas en artículos futuros, no se los pierdan!&lt;/p&gt;
&lt;p&gt;Gracias por visitar el blog y saludos!&lt;/p&gt;
&lt;p&gt;&lt;em&gt;Este post fue escrito utilizando IPython notebook. Pueden descargar este &lt;a href="https://github.com/relopezbriega/relopezbriega.github.io/blob/master/downloads/MachineLearningOverfitting.ipynb"&gt;notebook&lt;/a&gt; o ver su version estática en &lt;a href="http://nbviewer.ipython.org/github/relopezbriega/relopezbriega.github.io/blob/master/downloads/MachineLearningOverfitting.ipynb"&gt;nbviewer&lt;/a&gt;.&lt;/em&gt;&lt;/p&gt;

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&lt;/p&gt;</summary><category term="python"></category><category term="estadistica"></category><category term="programacion"></category><category term="analisis de datos"></category><category term="Machine Learning"></category><category term="Sobreajuste"></category></entry><entry><title>Ejemplo de Machine Learning con Python - Selección de atributos</title><link href="http://relopezbriega.github.io/blog/2016/04/15/ejemplo-de-machine-learning-con-python-seleccion-de-atributos/" rel="alternate"></link><published>2016-04-15T00:00:00-03:00</published><author><name>Raul E. Lopez Briega</name></author><id>tag:relopezbriega.github.io,2016-04-15:blog/2016/04/15/ejemplo-de-machine-learning-con-python-seleccion-de-atributos/</id><summary type="html">&lt;p&gt;
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&lt;h2 id="Introducci&amp;#243;n"&gt;Introducci&amp;#243;n&lt;a class="anchor-link" href="#Introducci&amp;#243;n"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;&lt;p&gt;Continuando donde nos quedamos en el artículo anterior &lt;a href="http://relopezbriega.github.io/blog/2016/04/08/ejemplo-de-machine-learning-con-python-preprocesamiento-y-exploracion/"&gt;Ejemplo de Machine Learning - preprocesamiento y exploración&lt;/a&gt;; ahora es tiempo de ingresar en el terreno de la &lt;a href="https://en.wikipedia.org/wiki/Feature_selection"&gt;selección de atributos&lt;/a&gt;.&lt;/p&gt;
&lt;h2 id="&amp;#191;En-qu&amp;#233;-consiste-la-selecci&amp;#243;n-de-atributos?"&gt;&amp;#191;En qu&amp;#233; consiste la selecci&amp;#243;n de atributos?&lt;a class="anchor-link" href="#&amp;#191;En-qu&amp;#233;-consiste-la-selecci&amp;#243;n-de-atributos?"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;&lt;p&gt;La &lt;a href="https://en.wikipedia.org/wiki/Feature_selection"&gt;selección de atributos&lt;/a&gt; es el proceso por el cual seleccionamos un subconjunto de atributos (representados por cada una de las columnas en un &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt; de forma tabular) que son más relevantes para la construcción del modelo predictivo sobre el que estamos trabajando.&lt;/p&gt;
&lt;p&gt;Este proceso, no se debe confundir con el de &lt;a href="https://en.wikipedia.org/wiki/Dimensionality_reduction"&gt;reducción de dimensiones&lt;/a&gt;; si bien ambos procesos buscan reducir el número de atributos en nuestro &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt;; este último lo hace por medio de la creación de nuevos atributos que son combinaciones de los anteriores; mientras que en el proceso de &lt;a href="https://en.wikipedia.org/wiki/Feature_selection"&gt;selección de atributos&lt;/a&gt;, intentamos incluir y excluir los atributos prácticamente sin modificarlos.&lt;/p&gt;
&lt;p&gt;El proceso de &lt;a href="https://en.wikipedia.org/wiki/Feature_selection"&gt;selección de atributos&lt;/a&gt; es tanto un arte como una ciencia, en donde el conocimiento sobre el problema y la intuición son sumamente importantes. El objetivo de la &lt;a href="https://en.wikipedia.org/wiki/Feature_selection"&gt;selección de atributos&lt;/a&gt; es triple: mejorar la capacidad predictiva de nuestro modelo, proporcionando modelos predictivos más rápidos y eficientes, y proporcionar una mejor comprensión del proceso subyacente que generó los datos. Los métodos de &lt;a href="https://en.wikipedia.org/wiki/Feature_selection"&gt;selección de atributos&lt;/a&gt; se pueden utilizar para identificar y eliminar los atributos innecesarios, irrelevantes y redundantes que no contribuyen a la exactitud del modelo predictivo o incluso puedan disminuir su precisión.&lt;/p&gt;
&lt;h2 id="Beneficios-de-la-selecci&amp;#243;n-de-atributos"&gt;Beneficios de la selecci&amp;#243;n de atributos&lt;a class="anchor-link" href="#Beneficios-de-la-selecci&amp;#243;n-de-atributos"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;&lt;p&gt;Uno de los principales beneficios de la &lt;a href="https://en.wikipedia.org/wiki/Feature_selection"&gt;selección de atributos&lt;/a&gt; esta plasmado por la famosa frase &lt;strong&gt;&lt;em&gt;"Menos es más"&lt;/em&gt;&lt;/strong&gt; del arquitecto &lt;a href="https://es.wikipedia.org/wiki/Ludwig_Mies_van_der_Rohe"&gt;Ludwig Mies van der Rohe&lt;/a&gt;, precursor del &lt;a href="https://es.wikipedia.org/wiki/Minimalismo"&gt;minimalismo&lt;/a&gt;. Menos atributos son deseables ya que reduce la complejidad del modelo, y un modelo más simple es más fácil de entender y explicar.&lt;/p&gt;
&lt;p&gt;Otros beneficios adicionales que nos proporciona una buena &lt;a href="https://en.wikipedia.org/wiki/Feature_selection"&gt;selección de atributos&lt;/a&gt; antes de comenzar con el armado del modelo, son:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Reduce el &lt;a href="https://es.wikipedia.org/wiki/Sobreajuste"&gt;sobreentrenamiento&lt;/a&gt;&lt;/strong&gt;: Menos datos redundantes significan menos oportunidades para tomar decisiones sobre la base de ruido.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Mejora la precisión&lt;/strong&gt;: Menos datos engañosos se convierten en una mejora en la exactitud del modelo.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Reduce el tiempo de entrenamiento&lt;/strong&gt;: Menos datos significa que los algoritmos aprenden más rápidamente.&lt;/li&gt;
&lt;/ul&gt;
&lt;h2 id="Selecci&amp;#243;n-de-atributos-univariante-o-multivariante"&gt;Selecci&amp;#243;n de atributos univariante o multivariante&lt;a class="anchor-link" href="#Selecci&amp;#243;n-de-atributos-univariante-o-multivariante"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;&lt;p&gt;Una cosa que no debemos pasar por alto en el proceso de &lt;a href="https://en.wikipedia.org/wiki/Feature_selection"&gt;selección de atributos&lt;/a&gt;, es la relación que puede existir entre ellos. Es decir que debemos considerar seleccionar o eliminar un atributo en forma individual (&lt;a href="https://en.wikipedia.org/wiki/Univariate_analysis"&gt;univariante&lt;/a&gt;) o un un grupo de atributos en forma conjunta (&lt;a href="https://es.wikipedia.org/wiki/An%C3%A1lisis_multivariante"&gt;multivariante&lt;/a&gt;). Esto también va a depender del problema con el que estemos tratando y del modelo que elijamos. Por ejemplo si elegimos como modelo un &lt;a href="https://es.wikipedia.org/wiki/Clasificador_bayesiano_ingenuo"&gt;clasificador bayesiano ingenuo&lt;/a&gt;, el modelo asume que cada atributo es independiente del resto, por lo tanto, podríamos utilizar un enfoque &lt;a href="https://en.wikipedia.org/wiki/Univariate_analysis"&gt;univariante&lt;/a&gt; sin problemas; en cambio si elegimos como modelo una &lt;a href="https://es.wikipedia.org/wiki/Red_neuronal_artificial"&gt;red neuronal&lt;/a&gt;, este último no asume la independencia de los atributos, sino que utiliza todas la que dispone; por lo tanto aquí deberíamos seguir un enfoque &lt;a href="https://es.wikipedia.org/wiki/An%C3%A1lisis_multivariante"&gt;multivariante&lt;/a&gt; para seleccionar los atributos.&lt;/p&gt;
&lt;h2 id="Algoritmos-para-selecci&amp;#243;n-de-atributos"&gt;Algoritmos para selecci&amp;#243;n de atributos&lt;a class="anchor-link" href="#Algoritmos-para-selecci&amp;#243;n-de-atributos"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;&lt;p&gt;Podemos encontrar dos clases generales de algoritmos de &lt;a href="https://en.wikipedia.org/wiki/Feature_selection"&gt;selección de atributos&lt;/a&gt;: los métodos de filtrado, y los métodos empaquetados.&lt;/p&gt;
&lt;h3 id="M&amp;#233;todos-de-filtrado"&gt;M&amp;#233;todos de filtrado&lt;a class="anchor-link" href="#M&amp;#233;todos-de-filtrado"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;Estos métodos aplican una medida estadística para asignar una puntuación a cada atributo. Los atributos luego son clasificados de acuerdo a su puntuación y son, o bien seleccionados para su conservación o eliminados del conjunto de datos. Los métodos de filtrado son a menudo &lt;a href="https://en.wikipedia.org/wiki/Univariate_analysis"&gt;univariantes&lt;/a&gt; y consideran a cada atributo en forma independiente, o con respecto a la variable dependiente.&lt;/p&gt;
&lt;p&gt;Ejemplos de estos métodos son: &lt;a href="https://es.wikipedia.org/wiki/Prueba_%CF%87%C2%B2"&gt;prueba de Chi cuadrado&lt;/a&gt;, &lt;a href="https://es.wikipedia.org/wiki/Prueba_F_de_Fisher"&gt;prueba F de Fisher&lt;/a&gt;, &lt;a href="https://en.wikipedia.org/wiki/Information_gain_ratio"&gt;ratio de ganancia de información&lt;/a&gt; y los &lt;a href="https://es.wikipedia.org/wiki/Correlaci%C3%B3n"&gt;coeficientes de correlación&lt;/a&gt;.&lt;/p&gt;
&lt;h3 id="M&amp;#233;todos-empaquetados"&gt;M&amp;#233;todos empaquetados&lt;a class="anchor-link" href="#M&amp;#233;todos-empaquetados"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;Estos métodos consideran la selección de un conjunto de atributos como un problema de búsqueda, en donde las diferentes combinaciones son evaluadas y comparadas. Para hacer estas evaluaciones se utiliza un modelo predictivo y luego se asigna una puntuación a cada combinación basada en la precisión del modelo.&lt;/p&gt;
&lt;p&gt;Un ejemplo de este método es el algoritmo de eliminación recursiva de atributos.&lt;/p&gt;
&lt;h2 id="Ejemplo"&gt;Ejemplo&lt;a class="anchor-link" href="#Ejemplo"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;&lt;p&gt;Pasamos ahora a ver como podemos aplicar todo esto al caso en el que veníamos trabajando en el el &lt;a href="http://relopezbriega.github.io/blog/2016/04/08/ejemplo-de-machine-learning-con-python-preprocesamiento-y-exploracion/"&gt;artículo anterior&lt;/a&gt;. Pero antes, terminemos con algunas tareas de preprocesamiento adicionales.&lt;/p&gt;

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&lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Importando las librerías que vamos a utilizar&lt;/span&gt;
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;pandas&lt;/span&gt; &lt;span class="kn"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;pd&lt;/span&gt;
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;numpy&lt;/span&gt; &lt;span class="kn"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;np&lt;/span&gt; 
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;matplotlib.pyplot&lt;/span&gt; &lt;span class="kn"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;plt&lt;/span&gt; 
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;seaborn&lt;/span&gt; &lt;span class="kn"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;sns&lt;/span&gt; 
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;sklearn.cross_validation&lt;/span&gt; &lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="n"&gt;train_test_split&lt;/span&gt;
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;sklearn.feature_selection&lt;/span&gt; &lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="n"&gt;SelectKBest&lt;/span&gt;
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;sklearn.feature_selection&lt;/span&gt; &lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="n"&gt;f_classif&lt;/span&gt;
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;sklearn.feature_selection&lt;/span&gt; &lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="n"&gt;RFE&lt;/span&gt;
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;sklearn.ensemble&lt;/span&gt; &lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="n"&gt;ExtraTreesClassifier&lt;/span&gt;

&lt;span class="c1"&gt;# graficos incrustados&lt;/span&gt;
&lt;span class="o"&gt;%&lt;/span&gt;&lt;span class="k"&gt;matplotlib&lt;/span&gt; inline

&lt;span class="c1"&gt;# parametros esteticos de seaborn&lt;/span&gt;
&lt;span class="n"&gt;sns&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_palette&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;deep&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;desat&lt;/span&gt;&lt;span class="o"&gt;=.&lt;/span&gt;&lt;span class="mi"&gt;6&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;sns&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_context&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;rc&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="p"&gt;{&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;figure.figsize&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt; &lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mi"&gt;8&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;4&lt;/span&gt;&lt;span class="p"&gt;)})&lt;/span&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# importando el dataset preprocesado.&lt;/span&gt;
&lt;span class="n"&gt;ONG_data&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;read_csv&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;LEARNING_procesado.csv&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;header&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="c1"&gt;# Agregando la columna AGE2&lt;/span&gt;
&lt;span class="n"&gt;AGE2&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;cut&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;AGE&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="nb"&gt;range&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;100&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;10&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt;
&lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;AGE2&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;AGE2&lt;/span&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Eliminar columnas con donaciones superiores a 60 (atípicos)&lt;/span&gt;
&lt;span class="n"&gt;ONG_data&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;DONOR_AMOUNT&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;&lt;/span&gt; &lt;span class="mi"&gt;60&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Convertir datos categoricos a numericos&lt;/span&gt;
&lt;span class="n"&gt;tipos&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;columns&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;to_series&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;groupby&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;dtypes&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;groups&lt;/span&gt;
&lt;span class="n"&gt;ctext&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;tipos&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;dtype&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;object&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)]&lt;/span&gt;

&lt;span class="k"&gt;for&lt;/span&gt; &lt;span class="n"&gt;c&lt;/span&gt; &lt;span class="ow"&gt;in&lt;/span&gt; &lt;span class="n"&gt;ctext&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt;
    &lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;c&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="n"&gt;_&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;factorize&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;c&lt;/span&gt;&lt;span class="p"&gt;])&lt;/span&gt;

&lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;AGE2&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="n"&gt;_&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;factorize&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;AGE2&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;])&lt;/span&gt;
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&lt;p&gt;Con estas manipulaciones lo que hicimos es cargar en memoria el &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt; que prepocesamos anteriormente, le agregamos la nueva columna AGE2, ya que es mejor tener la edad agrupada en rangos en lugar de individualmente, luego eliminamos los &lt;a href="https://es.wikipedia.org/wiki/Valor_at%C3%ADpico"&gt;valores atípicos&lt;/a&gt; que habíamos detectado; y por último, reemplazamos con su equivalente numérico a todas las &lt;a href="http://relopezbriega.github.io/blog/2016/02/29/analisis-de-datos-categoricos-con-python/"&gt;variables categóricas&lt;/a&gt;; ya que para los algoritmos de &lt;a href="http://scikit-learn.org/stable/"&gt;Scikit-learn&lt;/a&gt; es mucho más eficiente trabajar con variables numéricas.&lt;/p&gt;
&lt;p&gt;Ahora sí, ya estamos en condiciones de poder comenzar a aplicar algunos de los algoritmos de &lt;a href="https://en.wikipedia.org/wiki/Feature_selection"&gt;selección de atributos&lt;/a&gt;, comencemos con un simple algoritmo &lt;a href="https://en.wikipedia.org/wiki/Univariate_analysis"&gt;univariante&lt;/a&gt; que aplica el método de filtrado. Para esto vamos a utilizar los objetos &lt;code&gt;SelectKBest&lt;/code&gt; y &lt;code&gt;f_classif&lt;/code&gt; del paquete &lt;code&gt;sklearn.feature_selection&lt;/code&gt;.&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Separamos las columnas objetivo&lt;/span&gt;
&lt;span class="n"&gt;donor_flag&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;DONOR_FLAG&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;
&lt;span class="n"&gt;donor_amount&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;DONOR_AMOUNT&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;
&lt;span class="n"&gt;indice&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;IDX&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Aplicando el algoritmo univariante de prueba F.&lt;/span&gt;
&lt;span class="n"&gt;k&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="mi"&gt;15&lt;/span&gt;  &lt;span class="c1"&gt;# número de atributos a seleccionar&lt;/span&gt;
&lt;span class="n"&gt;entrenar&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;drop&lt;/span&gt;&lt;span class="p"&gt;([&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;DONOR_FLAG&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;DONOR_AMOUNT&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;IDX&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="n"&gt;axis&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;columnas&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="nb"&gt;list&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;entrenar&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;columns&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;values&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;seleccionadas&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;SelectKBest&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;f_classif&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;k&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;k&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;fit&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;entrenar&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;donor_flag&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;atrib&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;seleccionadas&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;get_support&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;span class="n"&gt;atributos&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;columnas&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;i&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt; &lt;span class="k"&gt;for&lt;/span&gt; &lt;span class="n"&gt;i&lt;/span&gt; &lt;span class="ow"&gt;in&lt;/span&gt; &lt;span class="nb"&gt;list&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;atrib&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;nonzero&lt;/span&gt;&lt;span class="p"&gt;()[&lt;/span&gt;&lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;])]&lt;/span&gt;
&lt;span class="n"&gt;atributos&lt;/span&gt;
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&lt;pre class="ipynb"&gt;[&amp;#39;ODATEDW&amp;#39;,
 &amp;#39;PEPSTRFL&amp;#39;,
 &amp;#39;HVP3&amp;#39;,
 &amp;#39;CARDPROM&amp;#39;,
 &amp;#39;NUMPROM&amp;#39;,
 &amp;#39;RAMNT_8&amp;#39;,
 &amp;#39;RAMNT_16&amp;#39;,
 &amp;#39;NGIFTALL&amp;#39;,
 &amp;#39;CARDGIFT&amp;#39;,
 &amp;#39;LASTGIFT&amp;#39;,
 &amp;#39;LASTDATE&amp;#39;,
 &amp;#39;FISTDATE&amp;#39;,
 &amp;#39;AVGGIFT&amp;#39;,
 &amp;#39;RFA_2F&amp;#39;,
 &amp;#39;RFA_2A&amp;#39;]&lt;/pre&gt;
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&lt;p&gt;Como podemos ver, el algoritmo nos seleccionó la cantidad de atributos que le indicamos; en este ejemplo decidimos seleccionar solo 15; obviamente, cuando armemos nuestro modelo final vamos a tomar un número mayor de atributos.&lt;/p&gt;
&lt;h3 id="&amp;#191;C&amp;#243;mo-funciona?"&gt;&amp;#191;C&amp;#243;mo funciona?&lt;a class="anchor-link" href="#&amp;#191;C&amp;#243;mo-funciona?"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;Este algoritmo selecciona a los mejores atributos basándose en una prueba estadística &lt;a href="https://en.wikipedia.org/wiki/Univariate_analysis"&gt;univariante&lt;/a&gt;. Al objeto &lt;code&gt;SelectKBest&lt;/code&gt; le pasamos la prueba estadística que vamos a a aplicar, en este caso una &lt;a href="https://es.wikipedia.org/wiki/Prueba_F_de_Fisher"&gt;prueba F&lt;/a&gt; definida por el objeto &lt;code&gt;f_classif&lt;/code&gt;, junto con el número de atributos a seleccionar. El algoritmo va a aplicar la prueba a todos los atributos y va a seleccionar los que mejor resultado obtuvieron.&lt;/p&gt;
&lt;p&gt;Ahora veamos como funciona el algoritmo de &lt;strong&gt;Eliminación Recursiva de atributos&lt;/strong&gt;. Para este caso, vamos a utilizar como nuestro modelo predictivo el algoritmo &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html"&gt;ExtraTreesClassifier&lt;/a&gt;.&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Algoritmo de Eliminación Recursiva de atributos con ExtraTrees&lt;/span&gt;
&lt;span class="n"&gt;modelo&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;ExtraTreesClassifier&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;span class="n"&gt;era&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;RFE&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;modelo&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;15&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;  &lt;span class="c1"&gt;# número de atributos a seleccionar&lt;/span&gt;
&lt;span class="n"&gt;era&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;era&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;fit&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;entrenar&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;donor_flag&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# imprimir resultados&lt;/span&gt;
&lt;span class="n"&gt;atrib&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;era&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;support_&lt;/span&gt;
&lt;span class="n"&gt;atributos&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;columnas&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;i&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt; &lt;span class="k"&gt;for&lt;/span&gt; &lt;span class="n"&gt;i&lt;/span&gt; &lt;span class="ow"&gt;in&lt;/span&gt; &lt;span class="nb"&gt;list&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;atrib&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;nonzero&lt;/span&gt;&lt;span class="p"&gt;()[&lt;/span&gt;&lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;])]&lt;/span&gt;
&lt;span class="n"&gt;atributos&lt;/span&gt;
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&lt;pre class="ipynb"&gt;[&amp;#39;OSOURCE&amp;#39;,
 &amp;#39;ZIP&amp;#39;,
 &amp;#39;VIETVETS&amp;#39;,
 &amp;#39;WWIIVETS&amp;#39;,
 &amp;#39;POP901&amp;#39;,
 &amp;#39;HV1&amp;#39;,
 &amp;#39;PEC2&amp;#39;,
 &amp;#39;TPE13&amp;#39;,
 &amp;#39;EIC4&amp;#39;,
 &amp;#39;EIC14&amp;#39;,
 &amp;#39;VC2&amp;#39;,
 &amp;#39;CARDPROM&amp;#39;,
 &amp;#39;MINRDATE&amp;#39;,
 &amp;#39;MAXRDATE&amp;#39;,
 &amp;#39;TIMELAG&amp;#39;]&lt;/pre&gt;
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&lt;h3 id="&amp;#191;C&amp;#243;mo-funciona?"&gt;&amp;#191;C&amp;#243;mo funciona?&lt;a class="anchor-link" href="#&amp;#191;C&amp;#243;mo-funciona?"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;En este algoritmo, dado un modelo predictivo que asigna un coeficiente de importancia a cada atributo (como ExtraTreesClassifier), el objetivo de la &lt;em&gt;Eliminación Recursiva de atributos&lt;/em&gt; es ir seleccionado en forma recursiva un número cada vez más pequeño de atributos. Primero comienza con todos los atributos del &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt; y luego en cada pasada va eliminando aquellos que tenga el menor coeficiente de importancia hasta alcanzar el número de atributos deseado.&lt;/p&gt;
&lt;p&gt;Si vemos los 15 atributos seleccionados por este otro algoritmo, existen muchas diferencias con los que selecciono el modelo anterior; en general, la Eliminación Recursiva de atributos suele ser mucho más precisa, pero también consume mucho más tiempo y recursos, ya que requiere que entrenemos a un &lt;em&gt;modelo predictivo&lt;/em&gt; para poder obtener sus resultados.&lt;/p&gt;
&lt;p&gt;Por último, también podríamos utilizar ese coeficiente de importancia que nos proporciona el modelo como una guía adicional para refinar nuestra &lt;a href="https://en.wikipedia.org/wiki/Feature_selection"&gt;selección de atributos&lt;/a&gt;.&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Importancia de atributos.&lt;/span&gt;
&lt;span class="n"&gt;modelo&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;fit&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;entrenar&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;donor_flag&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;modelo&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;feature_importances_&lt;/span&gt;&lt;span class="p"&gt;[:&lt;/span&gt;&lt;span class="mi"&gt;15&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;
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&lt;pre class="ipynb"&gt;array([  2.59616058e-03,   3.03252110e-03,   2.71464477e-03,
         2.51952243e-03,   2.34433609e-03,   6.01722535e-04,
         4.82782938e-04,   2.24732045e-03,   2.07872966e-03,
         1.29249803e-03,   1.17189225e-03,   9.77218420e-06,
         6.12421074e-04,   5.79137133e-05,   2.40594405e-03])&lt;/pre&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# 15 coeficientes más altos&lt;/span&gt;
&lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;sort&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;modelo&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;feature_importances_&lt;/span&gt;&lt;span class="p"&gt;)[::&lt;/span&gt;&lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;][:&lt;/span&gt;&lt;span class="mi"&gt;15&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

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&lt;pre class="ipynb"&gt;array([ 0.00369165,  0.00353173,  0.00348298,  0.00336095,  0.00331946,
        0.00331828,  0.00331345,  0.00326505,  0.00316758,  0.00316587,
        0.00313431,  0.00311334,  0.00310065,  0.00307309,  0.00304518])&lt;/pre&gt;
&lt;/div&gt;

&lt;/div&gt;

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&lt;p&gt;Analicemos algunos de estos atributos en forma individual para tener una idea de cuanto puede ser que aporten a la exactitud del modelo. Podríamos comparar por ejemplo, el promedio de donaciones que podríamos obtener con este atributo contra el promedio de todo el &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt;. Tomemos por ejemplo al atributo LASTGIFT que representa el importe de la última donación que realizó cada persona incluida en el conjunto de datos. En principio parece lógico que este atributo sea significativo para el modelo, ya que si donó en el pasado, hay bastantes posibilidades de que vuelva a donar en esta oportunidad.&lt;/p&gt;

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&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[11]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Probabilidad de ser donante de todo el dataset.&lt;/span&gt;
&lt;span class="n"&gt;prob_gral&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;DONOR_AMOUNT&lt;/span&gt; &lt;span class="o"&gt;&amp;gt;&lt;/span&gt; &lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;][&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;DONOR_AMOUNT&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;count&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;  \
                   &lt;span class="o"&gt;/&lt;/span&gt; &lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;DONOR_AMOUNT&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;count&lt;/span&gt;&lt;span class="p"&gt;())&lt;/span&gt; &lt;span class="o"&gt;*&lt;/span&gt; &lt;span class="mf"&gt;100.0&lt;/span&gt;
&lt;span class="n"&gt;prob_gral&lt;/span&gt; 
&lt;/pre&gt;&lt;/div&gt;

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&lt;div class="output_text output_subarea output_execute_result"&gt;
&lt;pre class="ipynb"&gt;5.0377358490566033&lt;/pre&gt;
&lt;/div&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Probabilidad de realizar donanción con LASTGIFT &amp;lt;= 10&lt;/span&gt;
&lt;span class="n"&gt;lastgift10&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="p"&gt;[(&lt;/span&gt;&lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;DONOR_AMOUNT&lt;/span&gt; &lt;span class="o"&gt;&amp;gt;&lt;/span&gt; &lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="o"&gt;&amp;amp;&lt;/span&gt;
                            &lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;LASTGIFT&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;=&lt;/span&gt; &lt;span class="mi"&gt;10&lt;/span&gt;&lt;span class="p"&gt;)][&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;DONOR_AMOUNT&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;count&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;  \
      &lt;span class="o"&gt;/&lt;/span&gt; &lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;LASTGIFT&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;=&lt;/span&gt; &lt;span class="mi"&gt;10&lt;/span&gt;&lt;span class="p"&gt;][&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;DONOR_AMOUNT&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;count&lt;/span&gt;&lt;span class="p"&gt;())&lt;/span&gt; &lt;span class="o"&gt;*&lt;/span&gt; &lt;span class="mf"&gt;100.0&lt;/span&gt;
&lt;span class="n"&gt;lastgift10&lt;/span&gt;
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&lt;pre class="ipynb"&gt;6.9347104389524157&lt;/pre&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# graficando los resultados&lt;/span&gt;
&lt;span class="n"&gt;lastgift&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;Series&lt;/span&gt;&lt;span class="p"&gt;({&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;promedio gral&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt; &lt;span class="n"&gt;prob_gral&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;lastgift&amp;lt;=10&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt; &lt;span class="n"&gt;lastgift10&lt;/span&gt;&lt;span class="p"&gt;})&lt;/span&gt;
&lt;span class="n"&gt;plot&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;lastgift&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;plot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;kind&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;barh&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
                   &lt;span class="n"&gt;color&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;blue&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;green&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;])&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_title&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Pobabilidad de donar&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

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"
&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing text_cell rendered"&gt;
&lt;div class="prompt input_prompt"&gt;
&lt;/div&gt;
&lt;div class="inner_cell"&gt;
&lt;div class="text_cell_render border-box-sizing rendered_html"&gt;
&lt;p&gt;Este último gráfico nos muestra claramente que con un valor del atributo LASTGIFT menor o igual a 10 las probabilidades de que esa persona realice una donación mejoran, pero veamos que pasa con el importe de la donación.&lt;/p&gt;

&lt;/div&gt;
&lt;/div&gt;
&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[14]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# importe promedio de donación general&lt;/span&gt;
&lt;span class="n"&gt;donacion_prom&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;DONOR_AMOUNT&lt;/span&gt; &lt;span class="o"&gt;&amp;gt;&lt;/span&gt; &lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;][&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;DONOR_AMOUNT&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;mean&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;span class="n"&gt;donacion_prom&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;div class="output_wrapper"&gt;
&lt;div class="output"&gt;


&lt;div class="output_area"&gt;
&lt;div class="prompt output_prompt"&gt;Out[14]:&lt;/div&gt;



&lt;div class="output_text output_subarea output_execute_result"&gt;
&lt;pre class="ipynb"&gt;14.889109446525177&lt;/pre&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[15]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# importe promedio de donación lastgift &amp;lt;= 10&lt;/span&gt;
&lt;span class="n"&gt;lastgift10_imp&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="p"&gt;[(&lt;/span&gt;&lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;DONOR_AMOUNT&lt;/span&gt; &lt;span class="o"&gt;&amp;gt;&lt;/span&gt; &lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="o"&gt;&amp;amp;&lt;/span&gt; 
                         &lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;LASTGIFT&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;=&lt;/span&gt; &lt;span class="mi"&gt;10&lt;/span&gt;&lt;span class="p"&gt;)][&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;DONOR_AMOUNT&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;mean&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;span class="n"&gt;lastgift10_imp&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;div class="output_wrapper"&gt;
&lt;div class="output"&gt;


&lt;div class="output_area"&gt;
&lt;div class="prompt output_prompt"&gt;Out[15]:&lt;/div&gt;



&lt;div class="output_text output_subarea output_execute_result"&gt;
&lt;pre class="ipynb"&gt;8.7553191489361701&lt;/pre&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[16]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# graficando los resultados&lt;/span&gt;
&lt;span class="n"&gt;lastgift&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;Series&lt;/span&gt;&lt;span class="p"&gt;({&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;imp promedio gral&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt; &lt;span class="n"&gt;donacion_prom&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
                      &lt;span class="s1"&gt;&amp;#39;lastgift&amp;lt;=10&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt; &lt;span class="n"&gt;lastgift10_imp&lt;/span&gt;&lt;span class="p"&gt;})&lt;/span&gt;
&lt;span class="n"&gt;plot&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;lastgift&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;plot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;kind&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;barh&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
                   &lt;span class="n"&gt;color&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;blue&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;green&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;
                  &lt;span class="p"&gt;)&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_title&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;importe promedio de donación&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
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&lt;p&gt;Aquí vemos, que si bien las probabilidades de que sea un donador mejoran, el importe que se dona esta por debajo del promedio. En el caso de este atributo podemos ver que existe una &lt;a href="https://es.wikipedia.org/wiki/Correlaci%C3%B3n"&gt;correlación&lt;/a&gt; inversa entre el importe de donación y la probabilidad de hacer una donación a la ONG.&lt;/p&gt;
&lt;p&gt;Hasta aquí llegamos en este artículo, la idea es que luego, cuando tengamos armado el modelo, podamos jugar con distintas combinaciones de atributos y ver como se comporta el modelo hasta alcanzar la combinación ideal de atributos. No se pierdan los próximos artículos!&lt;/p&gt;
&lt;p&gt;Saludos!&lt;/p&gt;
&lt;p&gt;&lt;em&gt;Este post fue escrito utilizando IPython notebook. Pueden descargar este &lt;a href="https://github.com/relopezbriega/relopezbriega.github.io/blob/master/downloads/MachineLearningPractica2.ipynb"&gt;notebook&lt;/a&gt; o ver su version estática en &lt;a href="http://nbviewer.ipython.org/github/relopezbriega/relopezbriega.github.io/blob/master/downloads/MachineLearningPractica2.ipynb"&gt;nbviewer&lt;/a&gt;.&lt;/em&gt;&lt;/p&gt;

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&lt;/p&gt;</summary><category term="python"></category><category term="estadistica"></category><category term="programacion"></category><category term="analisis de datos"></category><category term="Machine Learning"></category></entry><entry><title>Ejemplo de Machine Learning con Python - Preprocesamiento y exploración</title><link href="http://relopezbriega.github.io/blog/2016/04/08/ejemplo-de-machine-learning-con-python-preprocesamiento-y-exploracion/" rel="alternate"></link><published>2016-04-08T00:00:00-03:00</published><author><name>Raul E. Lopez Briega</name></author><id>tag:relopezbriega.github.io,2016-04-08:blog/2016/04/08/ejemplo-de-machine-learning-con-python-preprocesamiento-y-exploracion/</id><summary type="html">&lt;p&gt;
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&lt;p&gt;&lt;img alt="Machine Learning" title="Machine Learning" src="http://relopezbriega.github.io/images/machine-learning.jpg"&gt;&lt;/p&gt;

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&lt;h2 id="Introducci&amp;#243;n"&gt;Introducci&amp;#243;n&lt;a class="anchor-link" href="#Introducci&amp;#243;n"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;&lt;p&gt;En mi artículo &lt;a href="http://relopezbriega.github.io/blog/2015/10/10/machine-learning-con-python/"&gt;Machine Learning con Python&lt;/a&gt;, hice una breve introducción a los principales conceptos que debemos conocer de &lt;a href="http://relopezbriega.github.io/tag/machine-learning.html"&gt;Machine Learning&lt;/a&gt;. En este artículo, la idea es profundizar un poco más en ellos y presentar algunos conceptos nuevos con la ayuda de un ejemplo práctico.&lt;/p&gt;
&lt;h3 id="Descripci&amp;#243;n-del-ejemplo"&gt;Descripci&amp;#243;n del ejemplo&lt;a class="anchor-link" href="#Descripci&amp;#243;n-del-ejemplo"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;En el ejemplo que vamos a utilizar, vamos a imaginarnos que una organización sin fines de lucro soporta su
operación mediante la organización periódica de una campaña para recaudar fondos por correo. Esta organización ha creado una base de datos con más de 40 mil personas que por lo menos una vez en el pasado ha sido donante.
La campaña de recaudación de fondos se realiza mediante el envío a una lista de correo (o un subconjunto de ella) de un regalo simbólico y la solicitud de una donación.
Una vez que se planifica la campaña, el costo total de la misma se conoce de forma automática:&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;[número de potenciales donantes a contactar] x ([costo de regalo] + [costo de correo])&lt;/strong&gt;&lt;/p&gt;
&lt;p&gt;Sin embargo, el resultado de la recaudación de fondos depende tanto del número de donantes que responde a la campaña, como del importe medio de dinero que es donado.&lt;/p&gt;
&lt;p&gt;La idea es que, utilizando las técnicas de &lt;a href="http://relopezbriega.github.io/tag/machine-learning.html"&gt;Machine Learning&lt;/a&gt; sobre la base de datos de esta organización, podamos ayudarla a maximizar los beneficios de la campaña de recaudación, esto es, lograr el máximo importe posible de dinero recaudado, minimizando lo más que se pueda el costo total de la campaña.
Debemos tener en cuenta que un miembro de la organización le enviará el correo a un potencial donante, siempre que el rendimiento esperado del pedido excede el costo del correo con la solicitud de donación. Para nuestro ejemplo, el costo por donante de la campaña va a ser igual al &lt;strong&gt;[costo de regalo] + [costo de correo]&lt;/strong&gt;, y esto va a ser igual a &lt;strong&gt;\$ 0.75&lt;/strong&gt; por correo enviado. Los ingresos netos de la campaña se calculan como la suma &lt;strong&gt;(importe de donación real - \$ 0.75)&lt;/strong&gt; sobre todos los donantes a los que se ha enviado el correo. Nuestro objetivo es ayudar a esta organización sin fines de lucro a seleccionar de su lista de correo los donantes a los que debe abordar a los efectos de maximizar los beneficios de la campaña de recaudación.&lt;/p&gt;
&lt;h3 id="El-Dataset"&gt;El Dataset&lt;a class="anchor-link" href="#El-Dataset"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;El &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt; que vamos a utilizar, consiste en la base de datos de la organización sin fines de lucro con la lista de correo de los donantes de sus campañas anteriores. El mismo, ya lo hemos dividido en un &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt; de aprendizaje que se pueden descargar del siguiente &lt;a href="https://github.com/relopezbriega/relopezbriega.github.io/blob/master/downloads/LEARNING.csv"&gt;enlace&lt;/a&gt;; y un &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt; que vamos a utilizar para realizar las predicciones, el cual se lo pueden descargar desde este otro &lt;a href="https://github.com/relopezbriega/relopezbriega.github.io/blob/master/downloads/VALIDATION.csv"&gt;enlace&lt;/a&gt;.
Algunos otros datos a tener en cuenta, son los siguientes:&lt;/p&gt;
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&lt;li&gt;El &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt; de aprendizaje contiene 47720 registros y 481 columnas. La primera fila / cabecera del mismo contiene los nombres de cada campo.&lt;/li&gt;
&lt;li&gt;El &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt; de validación contiene 47692 registros y 479 columnas. Al igual que en el caso anterior, la primera fila contiene los nombres de cada campo.&lt;/li&gt;
&lt;li&gt;Los registros del &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt; de validación son idénticos a los registros del &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt; de aprendizaje, excepto que los valores para nuestros campos objetivo que necesitamos para el aprendizaje, no existen(es decir, las columnas DONOR_FLAG y DONOR_AMOUNT no están incluidas en el &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt; de validación).&lt;/li&gt;
&lt;li&gt;Los espacios en blanco en los campos de tipo texto y los puntos en los campos de tipo numérico corresponden a valores faltantes o perdidos.&lt;/li&gt;
&lt;li&gt;Cada registro tiene un identificador único de registro o índice (campo IDX). Para cada registro, hay dos variables objetivo (campos DONOR_FLAG y DONOR_AMOUNT). DONOR_FLAG es una variable binaria que indica si ese registro fue donante o no; mientras que DONOR_AMOUNT contiene el importe de la donación para los casos que fueron donantes.&lt;/li&gt;
&lt;li&gt;Algunos de los valores en el &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt; pueden contener errores de formato o de ingreso. Por lo que se deberían corregir o limpiar.&lt;/li&gt;
&lt;li&gt;Una descripción detallada del significado de cada columna del &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt;, la pueden encontrar en el siguiente &lt;a href="https://github.com/relopezbriega/relopezbriega.github.io/blob/master/downloads/describe_dataset.txt"&gt;enlace&lt;/a&gt;.&lt;/li&gt;
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&lt;h2 id="An&amp;#225;lisis-exploratorio-y-preprocesamiento"&gt;An&amp;#225;lisis exploratorio y preprocesamiento&lt;a class="anchor-link" href="#An&amp;#225;lisis-exploratorio-y-preprocesamiento"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;&lt;p&gt;El primer paso que deberíamos emprender, es realizar un pequeño &lt;a href="https://es.wikipedia.org/wiki/An%C3%A1lisis_exploratorio_de_datos"&gt;análisis exploratorio&lt;/a&gt; de nuestro &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt;; es decir, valernos de algunos herramientas de la &lt;a href="https://es.wikipedia.org/wiki/Estad%C3%ADstica"&gt;estadística&lt;/a&gt;, junto con algunas visualizaciones para entender un poco más los datos de los que disponemos. Veamos como podemos hacer esto.&lt;/p&gt;

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&lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Importando las librerías que vamos a utilizar&lt;/span&gt;
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;pandas&lt;/span&gt; &lt;span class="kn"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;pd&lt;/span&gt;
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;numpy&lt;/span&gt; &lt;span class="kn"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;np&lt;/span&gt; 
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;matplotlib.pyplot&lt;/span&gt; &lt;span class="kn"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;plt&lt;/span&gt; 
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;seaborn&lt;/span&gt; &lt;span class="kn"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;sns&lt;/span&gt; 
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;sklearn.preprocessing&lt;/span&gt; &lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="n"&gt;LabelEncoder&lt;/span&gt;

&lt;span class="c1"&gt;# graficos incrustados&lt;/span&gt;
&lt;span class="o"&gt;%&lt;/span&gt;&lt;span class="k"&gt;matplotlib&lt;/span&gt; inline

&lt;span class="c1"&gt;# parametros esteticos de seaborn&lt;/span&gt;
&lt;span class="n"&gt;sns&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_palette&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;deep&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;desat&lt;/span&gt;&lt;span class="o"&gt;=.&lt;/span&gt;&lt;span class="mi"&gt;6&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;sns&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_context&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;rc&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="p"&gt;{&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;figure.figsize&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt; &lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mi"&gt;8&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;4&lt;/span&gt;&lt;span class="p"&gt;)})&lt;/span&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# importando el dataset a un Dataframe de Pandas&lt;/span&gt;
&lt;span class="n"&gt;ONG_data&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;read_csv&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;LEARNING.csv&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;header&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Examinando las primeras 10 filas y 10 columnas del dataset&lt;/span&gt;
&lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;ix&lt;/span&gt;&lt;span class="p"&gt;[:&lt;/span&gt;&lt;span class="mi"&gt;10&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="p"&gt;:&lt;/span&gt;&lt;span class="mi"&gt;10&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;
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      &lt;th&gt;&lt;/th&gt;
      &lt;th&gt;ODATEDW&lt;/th&gt;
      &lt;th&gt;OSOURCE&lt;/th&gt;
      &lt;th&gt;TCODE&lt;/th&gt;
      &lt;th&gt;STATE&lt;/th&gt;
      &lt;th&gt;ZIP&lt;/th&gt;
      &lt;th&gt;MAILCODE&lt;/th&gt;
      &lt;th&gt;PVASTATE&lt;/th&gt;
      &lt;th&gt;DOB&lt;/th&gt;
      &lt;th&gt;NOEXCH&lt;/th&gt;
      &lt;th&gt;RECINHSE&lt;/th&gt;
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  &lt;tbody&gt;
    &lt;tr&gt;
      &lt;th&gt;0&lt;/th&gt;
      &lt;td&gt;8901&lt;/td&gt;
      &lt;td&gt;GRI&lt;/td&gt;
      &lt;td&gt;0&lt;/td&gt;
      &lt;td&gt;IL&lt;/td&gt;
      &lt;td&gt;61081&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
      &lt;td&gt;3712&lt;/td&gt;
      &lt;td&gt;0&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
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    &lt;tr&gt;
      &lt;th&gt;1&lt;/th&gt;
      &lt;td&gt;9401&lt;/td&gt;
      &lt;td&gt;NWN&lt;/td&gt;
      &lt;td&gt;0&lt;/td&gt;
      &lt;td&gt;LA&lt;/td&gt;
      &lt;td&gt;70611&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
      &lt;td&gt;0&lt;/td&gt;
      &lt;td&gt;0&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
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      &lt;th&gt;2&lt;/th&gt;
      &lt;td&gt;9401&lt;/td&gt;
      &lt;td&gt;MSD&lt;/td&gt;
      &lt;td&gt;1&lt;/td&gt;
      &lt;td&gt;TN&lt;/td&gt;
      &lt;td&gt;37127-&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
      &lt;td&gt;3211&lt;/td&gt;
      &lt;td&gt;0&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
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    &lt;tr&gt;
      &lt;th&gt;3&lt;/th&gt;
      &lt;td&gt;8901&lt;/td&gt;
      &lt;td&gt;ENQ&lt;/td&gt;
      &lt;td&gt;0&lt;/td&gt;
      &lt;td&gt;MN&lt;/td&gt;
      &lt;td&gt;56475&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
      &lt;td&gt;2603&lt;/td&gt;
      &lt;td&gt;0&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;4&lt;/th&gt;
      &lt;td&gt;9201&lt;/td&gt;
      &lt;td&gt;HCC&lt;/td&gt;
      &lt;td&gt;1&lt;/td&gt;
      &lt;td&gt;LA&lt;/td&gt;
      &lt;td&gt;70791&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
      &lt;td&gt;0&lt;/td&gt;
      &lt;td&gt;0&lt;/td&gt;
      &lt;td&gt;X&lt;/td&gt;
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    &lt;tr&gt;
      &lt;th&gt;5&lt;/th&gt;
      &lt;td&gt;9301&lt;/td&gt;
      &lt;td&gt;USB&lt;/td&gt;
      &lt;td&gt;1&lt;/td&gt;
      &lt;td&gt;UT&lt;/td&gt;
      &lt;td&gt;84720&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
      &lt;td&gt;2709&lt;/td&gt;
      &lt;td&gt;0&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
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    &lt;tr&gt;
      &lt;th&gt;6&lt;/th&gt;
      &lt;td&gt;9401&lt;/td&gt;
      &lt;td&gt;FRC&lt;/td&gt;
      &lt;td&gt;1&lt;/td&gt;
      &lt;td&gt;CA&lt;/td&gt;
      &lt;td&gt;90056&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
      &lt;td&gt;0&lt;/td&gt;
      &lt;td&gt;0&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;7&lt;/th&gt;
      &lt;td&gt;8801&lt;/td&gt;
      &lt;td&gt;PCH&lt;/td&gt;
      &lt;td&gt;2&lt;/td&gt;
      &lt;td&gt;IL&lt;/td&gt;
      &lt;td&gt;62376&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
      &lt;td&gt;5201&lt;/td&gt;
      &lt;td&gt;0&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
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    &lt;tr&gt;
      &lt;th&gt;8&lt;/th&gt;
      &lt;td&gt;8601&lt;/td&gt;
      &lt;td&gt;AMB&lt;/td&gt;
      &lt;td&gt;28&lt;/td&gt;
      &lt;td&gt;FL&lt;/td&gt;
      &lt;td&gt;32810&lt;/td&gt;
      &lt;td&gt;B&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
      &lt;td&gt;3601&lt;/td&gt;
      &lt;td&gt;0&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;9&lt;/th&gt;
      &lt;td&gt;9501&lt;/td&gt;
      &lt;td&gt;L15&lt;/td&gt;
      &lt;td&gt;1&lt;/td&gt;
      &lt;td&gt;NC&lt;/td&gt;
      &lt;td&gt;27850&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
      &lt;td&gt;0&lt;/td&gt;
      &lt;td&gt;0&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;10&lt;/th&gt;
      &lt;td&gt;8701&lt;/td&gt;
      &lt;td&gt;BBK&lt;/td&gt;
      &lt;td&gt;2&lt;/td&gt;
      &lt;td&gt;MN&lt;/td&gt;
      &lt;td&gt;55125&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
      &lt;td&gt;3601&lt;/td&gt;
      &lt;td&gt;0&lt;/td&gt;
      &lt;td&gt;&lt;/td&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Controlando la cantidad de registros&lt;/span&gt;
&lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;DONOR_AMOUNT&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;count&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
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&lt;pre class="ipynb"&gt;47720&lt;/pre&gt;
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&lt;p&gt;Como podemos ver, utilizando simples expresiones de &lt;a href="http://python.org/"&gt;Python&lt;/a&gt;, podemos cargar la base de datos de la ONG en un &lt;em&gt;&lt;a href="http://pandas.pydata.org/pandas-docs/stable/dsintro.html"&gt;Dataframe&lt;/a&gt;&lt;/em&gt; de &lt;a href="http://pandas.pydata.org/"&gt;Pandas&lt;/a&gt;; lo que nos va a permitir manipular los datos con suma facilidad. Comenzemos a explorar un poco más en detalle este &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt;!&lt;/p&gt;
&lt;p&gt;En primer lugar, lo que deberíamos hacer es controlar si existen valores faltantes o nulos; esto lo podemos realizar utilizando el método &lt;code&gt;isnull()&lt;/code&gt; del siguiente modo:&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Controlando valores nulos&lt;/span&gt;
&lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;isnull&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;any&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;any&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
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&lt;pre class="ipynb"&gt;True&lt;/pre&gt;
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&lt;p&gt;Como podemos ver, el método nos devuelve el valor "True", lo que indica que existen valores nulos en nuestro &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt;. Estos valores pueden tener una influencia significativa en nuestro modelo predictivo, por lo que siempre es una decisión importante determinar la forma en que los vamos a manejar. Las alternativas que tenemos son:&lt;/p&gt;
&lt;ol&gt;
&lt;li&gt;Dejarlos como están, lo que a la larga nos va a traer bastantes dolores de cabeza ya que en general los algoritmos no los suelen procesar correctamente y provocan errores.&lt;/li&gt;
&lt;li&gt;Eliminarlos, lo que es una alternativa viable aunque, dependiendo la cantidad de valores nulos, puede afectar significativamente el resultado final de nuestro modelo predictivo.&lt;/li&gt;
&lt;li&gt;Inferir su valor. En este caso, lo que podemos hacer es tratar de inferir el valor faltante y reemplazarlo por el valor inferido. Esta suele ser generalmente la mejor alternativa a seguir.&lt;/li&gt;
&lt;/ol&gt;
&lt;p&gt;En este ejemplo, yo voy a utilizar la última alternativa. Vamos a inferir los valores faltantes utilizando la &lt;a href="https://es.wikipedia.org/wiki/Media_aritm%C3%A9tica"&gt;media aritmética&lt;/a&gt; para los &lt;a href="http://relopezbriega.github.io/blog/2016/03/13/analisis-de-datos-cuantitativos-con-python/"&gt;datos cuantitativos&lt;/a&gt; y la &lt;a href="https://es.wikipedia.org/wiki/Moda_(estad%C3%ADstica)"&gt;moda&lt;/a&gt; para los &lt;a href="http://relopezbriega.github.io/blog/2016/02/29/analisis-de-datos-categoricos-con-python/"&gt;datos categóricos&lt;/a&gt;.&lt;/p&gt;
&lt;p&gt;Como vamos a utilizar dos métodos distintos para reemplazar a los valores faltantes, dependiendo de si son &lt;a href="http://relopezbriega.github.io/blog/2016/03/13/analisis-de-datos-cuantitativos-con-python/"&gt;numéricos&lt;/a&gt; o &lt;a href="http://relopezbriega.github.io/blog/2016/02/29/analisis-de-datos-categoricos-con-python/"&gt;categóricos&lt;/a&gt;, el primer paso que debemos realizar es tratar de identificar que columnas de nuestro &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt; corresponde a cada tipo de datos; para realizar esto vamos a utilizar el atributo &lt;code&gt;dtypes&lt;/code&gt; del &lt;em&gt;&lt;a href="http://pandas.pydata.org/pandas-docs/stable/dsintro.html"&gt;Dataframe&lt;/a&gt;&lt;/em&gt; de &lt;a href="http://pandas.pydata.org/"&gt;Pandas&lt;/a&gt;.&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Agrupando columnas por tipo de datos&lt;/span&gt;
&lt;span class="n"&gt;tipos&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;columns&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;to_series&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;groupby&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;dtypes&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;groups&lt;/span&gt;

&lt;span class="c1"&gt;# Armando lista de columnas categóricas&lt;/span&gt;
&lt;span class="n"&gt;ctext&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;tipos&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;dtype&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;object&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)]&lt;/span&gt;
&lt;span class="nb"&gt;len&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;ctext&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;  &lt;span class="c1"&gt;# cantidad de columnas con datos categóricos. &lt;/span&gt;
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&lt;pre class="ipynb"&gt;68&lt;/pre&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Armando lista de columnas numéricas&lt;/span&gt;
&lt;span class="n"&gt;columnas&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;columns&lt;/span&gt;  &lt;span class="c1"&gt;# lista de todas las columnas&lt;/span&gt;
&lt;span class="n"&gt;cnum&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="nb"&gt;list&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="nb"&gt;set&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;columnas&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt; &lt;span class="nb"&gt;set&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;ctext&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt;
&lt;span class="nb"&gt;len&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;cnum&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
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&lt;pre class="ipynb"&gt;413&lt;/pre&gt;
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&lt;p&gt;Ahora ya logramos separar a las 481 columnas que tiene nuestro &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt;. 68 columnas contienen &lt;a href="http://relopezbriega.github.io/blog/2016/02/29/analisis-de-datos-categoricos-con-python/"&gt;datos categóricos&lt;/a&gt; y 413 contienen &lt;a href="http://relopezbriega.github.io/blog/2016/03/13/analisis-de-datos-cuantitativos-con-python/"&gt;datos cuantitativos&lt;/a&gt;. Procedamos a inferir los valores faltantes.&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Completando valores faltantas datos cuantititavos&lt;/span&gt;
&lt;span class="k"&gt;for&lt;/span&gt; &lt;span class="n"&gt;c&lt;/span&gt; &lt;span class="ow"&gt;in&lt;/span&gt; &lt;span class="n"&gt;cnum&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt;
    &lt;span class="n"&gt;mean&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;c&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;mean&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
    &lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;c&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;c&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;fillna&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;mean&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Completando valores faltantas datos categóricos&lt;/span&gt;
&lt;span class="k"&gt;for&lt;/span&gt; &lt;span class="n"&gt;c&lt;/span&gt; &lt;span class="ow"&gt;in&lt;/span&gt; &lt;span class="n"&gt;ctext&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt;
    &lt;span class="n"&gt;mode&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;c&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;mode&lt;/span&gt;&lt;span class="p"&gt;()[&lt;/span&gt;&lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;
    &lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;c&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;c&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;fillna&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;mode&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Controlando que no hayan valores faltantes&lt;/span&gt;
&lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;isnull&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;any&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;any&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
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&lt;pre class="ipynb"&gt;False&lt;/pre&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Guardando el dataset preprocesado&lt;/span&gt;
&lt;span class="c1"&gt;# Save transform datasets&lt;/span&gt;
&lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;to_csv&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;LEARNING_procesado.csv&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;index&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="kc"&gt;False&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
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&lt;p&gt;Perfecto! Ahora tenemos un &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt; limpio de valores faltantes. Ya estamos listos para comenzar a explorar los datos, comencemos por determinar el porcentaje de personas que alguna vez fue donante de la ONG y están incluidos en la base de datos con la que estamos trabajando.&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Calculando el porcentaje de donantes sobre toda la base de datos&lt;/span&gt;
&lt;span class="n"&gt;porcent_donantes&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;DONOR_AMOUNT&lt;/span&gt; 
                             &lt;span class="o"&gt;&amp;gt;&lt;/span&gt; &lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;][&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;DONOR_AMOUNT&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;count&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt; &lt;span class="o"&gt;*&lt;/span&gt; &lt;span class="mf"&gt;1.0&lt;/span&gt;
       &lt;span class="o"&gt;/&lt;/span&gt; &lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;DONOR_AMOUNT&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;count&lt;/span&gt;&lt;span class="p"&gt;())&lt;/span&gt; &lt;span class="o"&gt;*&lt;/span&gt; &lt;span class="mf"&gt;100.0&lt;/span&gt;
&lt;span class="nb"&gt;print&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;El procentaje de donantes de la base de datos es &lt;/span&gt;&lt;span class="si"&gt;{0:.2f}&lt;/span&gt;&lt;span class="s2"&gt;%&amp;quot;&lt;/span&gt;
      &lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;format&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;porcent_donantes&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt;
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&lt;pre class="ipynb"&gt;El procentaje de donantes de la base de datos es 5.08%
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Grafico de totas del porcentaje de donantes&lt;/span&gt;
&lt;span class="c1"&gt;# Agrupando por DONOR_FLAG&lt;/span&gt;
&lt;span class="n"&gt;donantes&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;groupby&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;DONOR_FLAG&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;IDX&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;count&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt; 
&lt;span class="c1"&gt;# Creando las leyendas del grafico.&lt;/span&gt;
&lt;span class="n"&gt;labels&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="p"&gt;[&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;Donante&lt;/span&gt;&lt;span class="se"&gt;\n&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;&lt;/span&gt; &lt;span class="o"&gt;+&lt;/span&gt; &lt;span class="nb"&gt;str&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="nb"&gt;round&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;x&lt;/span&gt; &lt;span class="o"&gt;*&lt;/span&gt; &lt;span class="mf"&gt;1.0&lt;/span&gt; &lt;span class="o"&gt;/&lt;/span&gt; &lt;span class="n"&gt;donantes&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;sum&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt; &lt;span class="o"&gt;*&lt;/span&gt; 
                            &lt;span class="mf"&gt;100.0&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;2&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt; &lt;span class="o"&gt;+&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;%&amp;#39;&lt;/span&gt;  &lt;span class="k"&gt;for&lt;/span&gt; &lt;span class="n"&gt;x&lt;/span&gt; &lt;span class="ow"&gt;in&lt;/span&gt; &lt;span class="n"&gt;donantes&lt;/span&gt; &lt;span class="p"&gt;]&lt;/span&gt;
&lt;span class="n"&gt;labels&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;No &amp;#39;&lt;/span&gt; &lt;span class="o"&gt;+&lt;/span&gt; &lt;span class="n"&gt;labels&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;

&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;pie&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;donantes&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;labels&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;labels&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;title&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Porcion de donantes&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;show&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
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"
&gt;
&lt;/div&gt;

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&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[14]:&lt;/div&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Creando subset con solo los donates&lt;/span&gt;
&lt;span class="n"&gt;ONG_donantes&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;ONG_data&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;DONOR_AMOUNT&lt;/span&gt; &lt;span class="o"&gt;&amp;gt;&lt;/span&gt; &lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;

&lt;span class="c1"&gt;# cantidad de donantes&lt;/span&gt;
&lt;span class="nb"&gt;len&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;ONG_donantes&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
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&lt;div class="prompt output_prompt"&gt;Out[14]:&lt;/div&gt;



&lt;div class="output_text output_subarea output_execute_result"&gt;
&lt;pre class="ipynb"&gt;2423&lt;/pre&gt;
&lt;/div&gt;

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&lt;p&gt;Aquí podemos ver que el porcentaje de personas que fueron donantes en el pasado es realmente muy bajo, solo un 5 % del total de la base de datos (2423 personas). Este es un dato importante a tener en cuenta ya que al existir tanta diferencia entre las &lt;em&gt;clases&lt;/em&gt; a clasificar, esto puede afectar considerablemente a nuestro &lt;a href="https://es.wikipedia.org/wiki/Algoritmo"&gt;algoritmo&lt;/a&gt; de aprendizaje.
Exploremos también un poco más en detalle a este grupo pequeño de personas que fueron donantes; veamos por ejemplo como se dividen de acuerdo a la cantidad de dinero donado.&lt;/p&gt;

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&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[15]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Analizando el importe de donanciones&lt;/span&gt;
&lt;span class="c1"&gt;# Creando un segmentos de importes&lt;/span&gt;
&lt;span class="n"&gt;imp_segm&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;cut&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;ONG_donantes&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;DONOR_AMOUNT&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; 
                  &lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;10&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;  &lt;span class="mi"&gt;20&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;30&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;40&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;50&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;60&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;100&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;200&lt;/span&gt;&lt;span class="p"&gt;])&lt;/span&gt;
&lt;span class="c1"&gt;# Creando el grafico de barras desde pandas&lt;/span&gt;
&lt;span class="n"&gt;plot&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;value_counts&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;imp_segm&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;plot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;kind&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;bar&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                                      &lt;span class="n"&gt;title&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Importes de donacion&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plot&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_ylabel&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Cant de donantes&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plot&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_xlabel&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Rango de importes&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;show&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
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/x+G1t+ACA7I1QAAAABJRU5ErkJggg==
"
&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[16]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Agrupación por segmento segun importe donado.&lt;/span&gt;
&lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;value_counts&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;imp_segm&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;div class="output_wrapper"&gt;
&lt;div class="output"&gt;


&lt;div class="output_area"&gt;
&lt;div class="prompt output_prompt"&gt;Out[16]:&lt;/div&gt;



&lt;div class="output_text output_subarea output_execute_result"&gt;
&lt;pre class="ipynb"&gt;(0, 10]       1026
(10, 20]       921
(20, 30]       358
(30, 40]        53
(40, 50]        43
(60, 100]       15
(50, 60]         4
(100, 200]       3
dtype: int64&lt;/pre&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[17]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# importe de donación promedio&lt;/span&gt;
&lt;span class="n"&gt;ONG_donantes&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;DONOR_AMOUNT&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;mean&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;div class="output_wrapper"&gt;
&lt;div class="output"&gt;


&lt;div class="output_area"&gt;
&lt;div class="prompt output_prompt"&gt;Out[17]:&lt;/div&gt;



&lt;div class="output_text output_subarea output_execute_result"&gt;
&lt;pre class="ipynb"&gt;15.598237721832438&lt;/pre&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[18]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Gráfico de cajas del importe de donación&lt;/span&gt;
&lt;span class="n"&gt;sns&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;boxplot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="nb"&gt;list&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;ONG_donantes&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;DONOR_AMOUNT&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]))&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;title&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;importe de donación&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;show&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;div class="output_wrapper"&gt;
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&lt;/div&gt;

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&lt;div class="text_cell_render border-box-sizing rendered_html"&gt;
&lt;p&gt;Este análisis nos muestra que la mayor cantidad de donaciones caen en un rango de importes entre 0 y 30, siendo la donación promedio 15.60. También podemos ver que donaciones que superen un importe de 50 son casos realmente poco frecuentes, por lo que constituyen &lt;a href="https://es.wikipedia.org/wiki/Valor_at%C3%ADpico"&gt;valores atípicos&lt;/a&gt; y sería prudente eliminar estos casos al entrenar nuestro modelo para que no distorsionen los resultados.&lt;/p&gt;
&lt;p&gt;Otra exploración interesante que podríamos realizar sobre nuestro &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;dataset&lt;/a&gt; relacionado con los donantes, es ver como se divide este grupo en términos de género y edad. Comencemos con el género!&lt;/p&gt;

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&lt;div class="cell border-box-sizing code_cell rendered"&gt;
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&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[19]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Grafico del género de los donantes&lt;/span&gt;
&lt;span class="n"&gt;ONG_donantes&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;groupby&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;GENDER&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;size&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;plot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;kind&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;bar&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;title&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Distribución por género&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;show&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
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"
&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[20]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Donaciones segun el género&lt;/span&gt;
&lt;span class="n"&gt;ONG_donantes&lt;/span&gt;&lt;span class="p"&gt;[(&lt;/span&gt;&lt;span class="n"&gt;ONG_donantes&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;DONOR_AMOUNT&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;=&lt;/span&gt; &lt;span class="mi"&gt;50&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
             &lt;span class="o"&gt;&amp;amp;&lt;/span&gt; &lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;ONG_donantes&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;GENDER&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;isin&lt;/span&gt;&lt;span class="p"&gt;([&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;F&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;M&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;])&lt;/span&gt;
               &lt;span class="p"&gt;)][[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;DONOR_AMOUNT&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;GENDER&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;boxplot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;by&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;GENDER&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;title&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Donantes segun sexo&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;show&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;div class="output_wrapper"&gt;
&lt;div class="output"&gt;


&lt;div class="output_area"&gt;
&lt;div class="prompt"&gt;&lt;/div&gt;



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"
&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[21]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Media de impote donado por mujeres&lt;/span&gt;
&lt;span class="n"&gt;ONG_donantes&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;ONG_donantes&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;GENDER&lt;/span&gt; &lt;span class="o"&gt;==&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;F&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;][[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;DONOR_AMOUNT&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;mean&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;div class="output_wrapper"&gt;
&lt;div class="output"&gt;


&lt;div class="output_area"&gt;
&lt;div class="prompt output_prompt"&gt;Out[21]:&lt;/div&gt;



&lt;div class="output_text output_subarea output_execute_result"&gt;
&lt;pre class="ipynb"&gt;DONOR_AMOUNT    14.610311
dtype: float64&lt;/pre&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[22]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Media de impote donado por hombres&lt;/span&gt;
&lt;span class="n"&gt;ONG_donantes&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;ONG_donantes&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;GENDER&lt;/span&gt; &lt;span class="o"&gt;==&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;M&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;][[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;DONOR_AMOUNT&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;mean&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;div class="output_wrapper"&gt;
&lt;div class="output"&gt;


&lt;div class="output_area"&gt;
&lt;div class="prompt output_prompt"&gt;Out[22]:&lt;/div&gt;



&lt;div class="output_text output_subarea output_execute_result"&gt;
&lt;pre class="ipynb"&gt;DONOR_AMOUNT    16.81989
dtype: float64&lt;/pre&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing text_cell rendered"&gt;
&lt;div class="prompt input_prompt"&gt;
&lt;/div&gt;
&lt;div class="inner_cell"&gt;
&lt;div class="text_cell_render border-box-sizing rendered_html"&gt;
&lt;p&gt;Aquí vemos que las mujeres suelen estar más propensas a donar, aunque donan un importe promedio menor (14.61) al que donan los hombres (16.82). Veamos ahora como se comportan las donaciones respecto a la edad.&lt;/p&gt;

&lt;/div&gt;
&lt;/div&gt;
&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[23]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Distribución de la edad de los donantes&lt;/span&gt;
&lt;span class="n"&gt;ONG_donantes&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;AGE&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;hist&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_title&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Distribución de donantes segun edad&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;show&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;div class="output_wrapper"&gt;
&lt;div class="output"&gt;


&lt;div class="output_area"&gt;
&lt;div class="prompt"&gt;&lt;/div&gt;



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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Agrupando la edad por rango de a 10&lt;/span&gt;
&lt;span class="n"&gt;AGE2&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;cut&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;ONG_donantes&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;AGE&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="nb"&gt;range&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;100&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;10&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt;
&lt;span class="n"&gt;ONG_donantes&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;AGE2&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;AGE2&lt;/span&gt;

&lt;span class="c1"&gt;# Gráfico de barras de donaciones por edad&lt;/span&gt;
&lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;value_counts&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;AGE2&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;plot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;kind&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;bar&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;title&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Donaciones por edad&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;show&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

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&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[25]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Importes de donación por grango de edad&lt;/span&gt;
&lt;span class="n"&gt;ONG_donantes&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;ONG_donantes&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;DONOR_AMOUNT&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;=&lt;/span&gt; &lt;span class="mi"&gt;50&lt;/span&gt;&lt;span class="p"&gt;][[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;DONOR_AMOUNT&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
                                               &lt;span class="s1"&gt;&amp;#39;AGE2&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;boxplot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;by&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;AGE2&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;title&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Importe de donación por edad&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;show&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

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&lt;p&gt;En este último análisis podemos ver que la mayor cantidad de los donantes son personas de entre 60 y 70 años, aunque la media de importe donado más alta la tienen las personas que van desde los 30 a los 60 años.&lt;/p&gt;

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&lt;p&gt;Con esto concluyo este análisis; en próximos artículos voy a continuar con el ejemplo completando los restantes pasos que incluye un proyecto de &lt;a href="http://relopezbriega.github.io/tag/machine-leraning.html"&gt;Machine Learning&lt;/a&gt; hasta concluir el modelo y poder utilizarlo para realizar predicciones (selección de atributos - armado de modelo - entrenamiento - evaluación - métricas - predicción). Espero lo hayan disfrutado tanto como yo disfrute al escribirlo!&lt;/p&gt;
&lt;p&gt;Saludos!&lt;/p&gt;
&lt;p&gt;&lt;em&gt;Este post fue escrito utilizando IPython notebook. Pueden descargar este &lt;a href="https://github.com/relopezbriega/relopezbriega.github.io/blob/master/downloads/MachineLearningPractica.ipynb"&gt;notebook&lt;/a&gt; o ver su version estática en &lt;a href="http://nbviewer.ipython.org/github/relopezbriega/relopezbriega.github.io/blob/master/downloads/MachineLearningPractica.ipynb"&gt;nbviewer&lt;/a&gt;.&lt;/em&gt;&lt;/p&gt;

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&lt;/p&gt;</summary><category term="python"></category><category term="estadistica"></category><category term="programacion"></category><category term="matematica"></category><category term="analisis de datos"></category><category term="Machine Learning"></category></entry><entry><title>Machine Learning con Python</title><link href="http://relopezbriega.github.io/blog/2015/10/10/machine-learning-con-python/" rel="alternate"></link><published>2015-10-10T00:00:00-03:00</published><author><name>Raul E. Lopez Briega</name></author><id>tag:relopezbriega.github.io,2015-10-10:blog/2015/10/10/machine-learning-con-python/</id><summary type="html">&lt;p&gt;
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&lt;p&gt;&lt;img alt="Machine Learning" title="Machine Learning" src="http://relopezbriega.github.io/images/machine-learning.jpg"&gt;&lt;/p&gt;

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&lt;p&gt;Una de las ramas de estudio que cada vez esta ganando más popularidad dentro de las &lt;a href="https://es.wikipedia.org/wiki/Ciencias_de_la_computaci%C3%B3n"&gt;ciencias de la computación&lt;/a&gt; es el &lt;strong&gt;&lt;em&gt;aprendizaje automático&lt;/em&gt;&lt;/strong&gt; o &lt;strong&gt;&lt;em&gt;&lt;a href="https://es.wikipedia.org/wiki/Aprendizaje_autom%C3%A1tico"&gt;Machine Learning&lt;/a&gt;&lt;/em&gt;&lt;/strong&gt;. Muchos de los servicios que utilizamos en nuestro día a día como google, gmail, netflix, spotify o amazon se valen de las herramientas que les brinda el &lt;a href="https://es.wikipedia.org/wiki/Aprendizaje_autom%C3%A1tico"&gt;Machine Learning&lt;/a&gt; para alcanzar un servicio cada vez más personalizado y lograr así ventajas competitivas sobre sus rivales.&lt;/p&gt;

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&lt;h2 id="Qu&amp;#233;-es-Machine-Learning?"&gt;Qu&amp;#233; es Machine Learning?&lt;a class="anchor-link" href="#Qu&amp;#233;-es-Machine-Learning?"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;
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&lt;p&gt;Pero, ¿qué es exactamente &lt;a href="https://es.wikipedia.org/wiki/Aprendizaje_autom%C3%A1tico"&gt;Machine Learning&lt;/a&gt;?. El &lt;a href="https://es.wikipedia.org/wiki/Aprendizaje_autom%C3%A1tico"&gt;Machine Learning&lt;/a&gt; es el diseño y estudio de las herramientas informáticas que utilizan la experiencia pasada para tomar decisiones futuras; es el estudio de programas que pueden aprenden de los datos. El objetivo fundamental del &lt;a href="https://es.wikipedia.org/wiki/Aprendizaje_autom%C3%A1tico"&gt;Machine Learning&lt;/a&gt; es &lt;strong&gt;&lt;em&gt;generalizar, o inducir una regla desconocida a partir de ejemplos donde esa regla es aplicada&lt;/em&gt;&lt;/strong&gt;. El ejemplo más típico donde podemos ver el uso del &lt;a href="https://es.wikipedia.org/wiki/Aprendizaje_autom%C3%A1tico"&gt;Machine Learning&lt;/a&gt; es en el filtrado de los correo basura o spam. Mediante la observación de miles de correos electrónicos que han sido marcados previamente como basura, los filtros de spam aprenden a clasificar los mensajes nuevos. El &lt;a href="https://es.wikipedia.org/wiki/Aprendizaje_autom%C3%A1tico"&gt;Machine Learning&lt;/a&gt; combina conceptos y técnicas de diferentes áreas del conocimiento, como las &lt;a href="https://es.wikipedia.org/wiki/Matem%C3%A1ticas"&gt;matemáticas&lt;/a&gt;, &lt;a href="https://es.wikipedia.org/wiki/Estad%C3%ADstica"&gt;estadísticas&lt;/a&gt; y las &lt;a href="https://es.wikipedia.org/wiki/Ciencias_de_la_computaci%C3%B3n"&gt;ciencias de la computación&lt;/a&gt;; por tal motivo, hay muchas maneras de aprender la disciplina.&lt;/p&gt;

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&lt;h2 id="Tipos-de-Machine-Learning"&gt;Tipos de Machine Learning&lt;a class="anchor-link" href="#Tipos-de-Machine-Learning"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;&lt;p&gt;El &lt;a href="https://es.wikipedia.org/wiki/Aprendizaje_autom%C3%A1tico"&gt;Machine Learning&lt;/a&gt; tiene una amplia gama de aplicaciones, incluyendo motores de búsqueda, diagnósticos médicos, detección de fraude en el uso de tarjetas de crédito, análisis del mercado de valores, clasificación de secuencias de ADN, reconocimiento del habla y del lenguaje escrito, juegos y robótica. Pero para poder abordar cada uno de estos temas es crucial en primer lugar distingir los distintos tipos de problemas de &lt;a href="https://es.wikipedia.org/wiki/Aprendizaje_autom%C3%A1tico"&gt;Machine Learning&lt;/a&gt; con los que nos podemos encontrar.&lt;/p&gt;
&lt;h3 id="Aprendizaje-supervisado"&gt;Aprendizaje supervisado&lt;a class="anchor-link" href="#Aprendizaje-supervisado"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;En los problemas de &lt;a href="https://es.wikipedia.org/wiki/Aprendizaje_supervisado"&gt;aprendizaje supervisado&lt;/a&gt; se enseña o entrena al &lt;a href="https://es.wikipedia.org/wiki/Algoritmo"&gt;algoritmo&lt;/a&gt; a partir de datos que ya vienen etiquetados con la respuesta correcta. Cuanto mayor es el conjunto de datos más el &lt;a href="https://es.wikipedia.org/wiki/Algoritmo"&gt;algoritmo&lt;/a&gt; puede aprender sobre el tema. Una vez concluído el entrenamiento, se le brindan nuevos datos, ya sin las etiquetas de las respuestas correctas, y el &lt;a href="https://es.wikipedia.org/wiki/Algoritmo"&gt;algoritmo&lt;/a&gt; de aprendizaje utiliza la experiencia pasada que adquirió durante la etapa de entrenamiento para predecir un resultado. Esto es similar al método de aprendizaje que se utiliza en las escuelas, donde se nos enseñan problemas y las formas de resolverlos, para que luego podamos aplicar los mismos métodos en situaciones similares.&lt;/p&gt;
&lt;h3 id="Aprendizaje-no-supervisado"&gt;Aprendizaje no supervisado&lt;a class="anchor-link" href="#Aprendizaje-no-supervisado"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;En los problemas de &lt;a href="https://es.wikipedia.org/wiki/Aprendizaje_no_supervisado"&gt;aprendizaje no supervisado&lt;/a&gt; el &lt;a href="https://es.wikipedia.org/wiki/Algoritmo"&gt;algoritmo&lt;/a&gt; es entrenado usando un conjunto de datos que no tiene ninguna etiqueta; en este caso, nunca se le dice al &lt;a href="https://es.wikipedia.org/wiki/Algoritmo"&gt;algoritmo&lt;/a&gt; lo que representan los datos. La idea es que el &lt;a href="https://es.wikipedia.org/wiki/Algoritmo"&gt;algoritmo&lt;/a&gt; pueda encontrar por si solo patrones que ayuden a entender el conjunto de datos. El &lt;a href="https://es.wikipedia.org/wiki/Aprendizaje_no_supervisado"&gt;aprendizaje no supervisado&lt;/a&gt; es similar al método que utilizamos para aprender a hablar cuando somos bebes, en un principio escuchamos hablar a nuestros padres y no entendemos nada; pero a medida que vamos escuchando miles de conversaciones, nuestro cerebro comenzará a formar un modelo sobre cómo funciona el lenguaje y comenzaremos a reconocer patrones y a esperar ciertos sonidos.&lt;/p&gt;
&lt;h3 id="Aprendizaje-por-refuerzo"&gt;Aprendizaje por refuerzo&lt;a class="anchor-link" href="#Aprendizaje-por-refuerzo"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;En los problemas de aprendizaje por refuerzo, el &lt;a href="https://es.wikipedia.org/wiki/Algoritmo"&gt;algoritmo&lt;/a&gt; aprende observando el mundo que le rodea. Su información de entrada es el feedback o retroalimentación que obtiene del mundo exterior como respuesta a sus acciones. Por lo tanto, el sistema aprende a base de ensayo-error. Un buen ejemplo de este tipo de aprendizaje lo podemos encontrar en los juegos, donde vamos probando nuevas estrategias y vamos seleccionando y perfeccionando aquellas que nos ayudan a ganar el juego. A medida que vamos adquiriendo más practica, el efecto acumulativo del refuerzo a nuestras acciones victoriosas terminará creando una estrategia ganadora.&lt;/p&gt;

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&lt;h2 id="Sobreentrenamiento"&gt;Sobreentrenamiento&lt;a class="anchor-link" href="#Sobreentrenamiento"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;&lt;p&gt;Como mencionamos cuando definimos al &lt;a href="https://es.wikipedia.org/wiki/Aprendizaje_autom%C3%A1tico"&gt;Machine Learning&lt;/a&gt;, la idea fundamental es encontrar patrones que podamos generalizar para luego poder aplicar esta generalización sobre los casos que todavía no hemos observado y realizar predicciones. Pero también puede ocurrir que durante el entrenamiento solo descubramos casualidades en los datos que se parecen a patrones interesantes, pero que no generalicen. Esto es lo que se conoce con el nombre de &lt;em&gt;&lt;a href="https://es.wikipedia.org/wiki/Sobreajuste"&gt;sobreentrenamiento&lt;/a&gt; o sobreajuste&lt;/em&gt;.&lt;/p&gt;
&lt;p&gt;El &lt;em&gt;&lt;a href="https://es.wikipedia.org/wiki/Sobreajuste"&gt;sobreentrenamiento&lt;/a&gt;&lt;/em&gt; es la tendencia que tienen la mayoría de los &lt;a href="https://es.wikipedia.org/wiki/Algoritmo"&gt;algoritmos&lt;/a&gt; de &lt;a href="https://es.wikipedia.org/wiki/Aprendizaje_autom%C3%A1tico"&gt;Machine Learning&lt;/a&gt; a ajustarse a unas características muy específicas de los datos de entrenamiento que no tienen relación causal con la &lt;em&gt;función objetivo&lt;/em&gt; que estamos buscando para generalizar. El ejemplo más extremo de un modelo &lt;em&gt;sobreentrenado&lt;/em&gt; es un modelo que solo memoriza las respuestas correctas; este modelo al ser utilizado con datos que nunca antes ha visto va a tener un rendimiento azaroso, ya que nunca logró generalizar un patrón para predecir.&lt;/p&gt;
&lt;h3 id="Como-evitar-el-sobreentrenamiento"&gt;Como evitar el sobreentrenamiento&lt;a class="anchor-link" href="#Como-evitar-el-sobreentrenamiento"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;Como mencionamos anteriormente, todos los modelos de &lt;a href="https://es.wikipedia.org/wiki/Aprendizaje_autom%C3%A1tico"&gt;Machine Learning&lt;/a&gt; tienen tendencia al &lt;em&gt;&lt;a href="https://es.wikipedia.org/wiki/Sobreajuste"&gt;sobreentrenamiento&lt;/a&gt;&lt;/em&gt;; es por esto que debemos aprender a convivir con el mismo y tratar de tomar medidas preventivas para reducirlo lo más posible. Las dos principales estrategias para lidiar son el &lt;em&gt;&lt;a href="https://es.wikipedia.org/wiki/Sobreajuste"&gt;sobreentrenamiento&lt;/a&gt;&lt;/em&gt; son: la &lt;strong&gt;&lt;em&gt;retención de datos&lt;/em&gt;&lt;/strong&gt; y la &lt;strong&gt;&lt;em&gt;&lt;a href="https://es.wikipedia.org/wiki/Validaci%C3%B3n_cruzada"&gt;validación cruzada&lt;/a&gt;&lt;/em&gt;&lt;/strong&gt;.&lt;/p&gt;
&lt;p&gt;En el primer caso, la idea es dividir nuestro &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;conjunto de datos&lt;/a&gt;, en uno o varios conjuntos de entrenamiento y otro/s conjuntos de evaluación. Es decir, que no le vamos a pasar todos nuestros datos al &lt;a href="https://es.wikipedia.org/wiki/Algoritmo"&gt;algoritmo&lt;/a&gt; durante el entrenamiento, sino que vamos a &lt;em&gt;retener&lt;/em&gt; una parte de los datos de entrenamiento para realizar una evaluación de la efectividad del modelo. Con esto lo que buscamos es evitar que los mismos datos que usamos para entrenar sean los mismos que utilizamos para evaluar. De esta forma vamos a poder analizar con más precisión como el modelo se va comportando a medida que más lo vamos entrenando y poder detectar el punto crítico en el que el modelo deja de generalizar y comienza a &lt;em&gt;sobreajustarse&lt;/em&gt; a los datos de entrenamiento.&lt;/p&gt;
&lt;p&gt;La &lt;em&gt;&lt;a href="https://es.wikipedia.org/wiki/Validaci%C3%B3n_cruzada"&gt;validación cruzada&lt;/a&gt;&lt;/em&gt; es un procedimiento más sofisticado que el anterior. En lugar de solo obtener una simple estimación de la efectividad de la &lt;em&gt;generalización&lt;/em&gt;; la idea es realizar un análisis estadístico para obtener otras medidas del rendimiento estimado, como la media y la varianza, y así poder entender cómo se espera que el rendimiento varíe a través de los distintos conjuntos de datos. Esta variación es fundamental para la evaluación de la confianza en la estimación del rendimiento.
La &lt;em&gt;&lt;a href="https://es.wikipedia.org/wiki/Validaci%C3%B3n_cruzada"&gt;validación cruzada&lt;/a&gt;&lt;/em&gt; también hace un mejor uso de un conjunto de datos limitado; ya que a diferencia de la simple división de los datos en uno el entrenamiento y otro de evaluación; la &lt;em&gt;&lt;a href="https://es.wikipedia.org/wiki/Validaci%C3%B3n_cruzada"&gt;validación cruzada&lt;/a&gt;&lt;/em&gt; calcula sus estimaciones sobre todo el &lt;a href="https://es.wikipedia.org/wiki/Conjunto_de_datos"&gt;conjunto de datos&lt;/a&gt; mediante la realización de múltiples divisiones e intercambios sistemáticos entre datos de entrenamiento y datos de evaluación.&lt;/p&gt;

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&lt;h2 id="Pasos-para-construir-un-modelo-de-machine-learning"&gt;Pasos para construir un modelo de machine learning&lt;a class="anchor-link" href="#Pasos-para-construir-un-modelo-de-machine-learning"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;&lt;p&gt;Construir un modelo de &lt;a href="https://es.wikipedia.org/wiki/Aprendizaje_autom%C3%A1tico"&gt;Machine Learning&lt;/a&gt;, no se reduce solo a utilizar un &lt;a href="https://es.wikipedia.org/wiki/Algoritmo"&gt;algoritmo&lt;/a&gt; de aprendizaje o utilizar una librería de &lt;a href="https://es.wikipedia.org/wiki/Aprendizaje_autom%C3%A1tico"&gt;Machine Learning&lt;/a&gt;; sino que es todo un proceso que suele involucrar los siguientes pasos:&lt;/p&gt;
&lt;ol&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Recolectar los datos&lt;/strong&gt;. Podemos recolectar los datos desde muchas fuentes, podemos por ejemplo extraer los datos de un sitio web o obtener los datos utilizando una &lt;a href="https://es.wikipedia.org/wiki/Interfaz_de_programaci%C3%B3n_de_aplicaciones"&gt;API&lt;/a&gt; o desde una base de datos. Podemos también utilizar otros dispositivos que recolectan los datos por nosotros; o utilizar datos que son de dominio público. El número de opciones que tenemos para recolectar datos no tiene fin!. Este paso parece obvio, pero es uno de los que más complicaciones trae y más tiempo consume.&lt;/p&gt;
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&lt;li&gt;&lt;p&gt;&lt;strong&gt;Preprocesar los datos&lt;/strong&gt;. Una vez que tenemos los datos, tenemos que asegurarnos que tiene el formato correcto para nutrir nuestro &lt;a href="https://es.wikipedia.org/wiki/Algoritmo"&gt;algoritmo&lt;/a&gt; de aprendizaje. Es prácticamente inevitable tener que realizar varias tareas de preprocesamiento antes de poder utilizar los datos. Igualmente este punto suele ser mucho más sencillo que el paso anterior.&lt;/p&gt;
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&lt;li&gt;&lt;p&gt;&lt;strong&gt;Explorar los datos&lt;/strong&gt;. Una vez que ya tenemos los datos y están con el formato correcto, podemos realizar un pre análisis para corregir los casos de valores faltantes o intentar encontrar a simple vista algún patrón en los mismos que nos facilite la construcción del modelo. En esta etapa suelen ser de mucha utilidad las medidas estadísticas y los gráficos en 2 y 3 dimensiones para tener una idea visual de como se comportan nuestros datos. En este punto podemos detectar &lt;a href="https://es.wikipedia.org/wiki/Valor_at%C3%ADpico"&gt;valores atípicos&lt;/a&gt; que debamos descartar; o encontrar las características que más influencia tienen para realizar una predicción.&lt;/p&gt;
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&lt;li&gt;&lt;p&gt;&lt;strong&gt;Entrenar el &lt;a href="https://es.wikipedia.org/wiki/Algoritmo"&gt;algoritmo&lt;/a&gt;&lt;/strong&gt;. Aquí es donde comenzamos a utilizar las técnicas de &lt;a href="https://es.wikipedia.org/wiki/Aprendizaje_autom%C3%A1tico"&gt;Machine Learning&lt;/a&gt; realmente. En esta etapa nutrimos al o los &lt;a href="https://es.wikipedia.org/wiki/Algoritmo"&gt;algoritmos&lt;/a&gt; de aprendizaje con los datos que venimos procesando en las etapas anteriores. La idea es que los &lt;a href="https://es.wikipedia.org/wiki/Algoritmo"&gt;algoritmos&lt;/a&gt; puedan extraer información útil de los datos que le pasamos para luego poder hacer predicciones.&lt;/p&gt;
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&lt;li&gt;&lt;p&gt;&lt;strong&gt;Evaluar el &lt;a href="https://es.wikipedia.org/wiki/Algoritmo"&gt;algoritmo&lt;/a&gt;&lt;/strong&gt;. En esta etapa ponemos a prueba la información o conocimiento que el &lt;a href="https://es.wikipedia.org/wiki/Algoritmo"&gt;algoritmo&lt;/a&gt; obtuvo del entrenamiento del paso anterior. Evaluamos que tan preciso es el algoritmo en sus predicciones y si no estamos muy conforme con su rendimiento, podemos volver a la etapa anterior y continuar entrenando el &lt;a href="https://es.wikipedia.org/wiki/Algoritmo"&gt;algoritmo&lt;/a&gt; cambiando algunos parámetros hasta lograr un rendimiento aceptable.&lt;/p&gt;
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&lt;li&gt;&lt;p&gt;&lt;strong&gt;Utilizar el modelo&lt;/strong&gt;. En esta ultima etapa, ya ponemos a nuestro modelo a enfrentarse al problema real. Aquí también podemos medir su rendimiento, lo que tal vez nos obligue a revisar todos los pasos anteriores.&lt;/p&gt;
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&lt;h2 id="Librer&amp;#237;as-de-Python-para-machine-learning"&gt;Librer&amp;#237;as de Python para machine learning&lt;a class="anchor-link" href="#Librer&amp;#237;as-de-Python-para-machine-learning"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;&lt;p&gt;Como siempre me gusta comentar, una de las grandes ventajas que ofrece &lt;a href="http://python.org/"&gt;Python&lt;/a&gt; sobre otros lenguajes de programación; es lo grande y prolifera que es la comunidad de desarrolladores que lo rodean; comunidad que ha contribuido con una gran variedad de librerías de primer nivel que extienden la funcionalidades del lenguaje. Para el caso de &lt;a href="https://es.wikipedia.org/wiki/Aprendizaje_autom%C3%A1tico"&gt;Machine Learning&lt;/a&gt;, las principales librerías que podemos utilizar son:&lt;/p&gt;
&lt;h3 id="Scikit-Learn"&gt;Scikit-Learn&lt;a class="anchor-link" href="#Scikit-Learn"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;&lt;a href="http://scikit-learn.org/stable/"&gt;Scikit-learn&lt;/a&gt; es la principal librería que existe para trabajar con &lt;a href="https://es.wikipedia.org/wiki/Aprendizaje_autom%C3%A1tico"&gt;Machine Learning&lt;/a&gt;, incluye la implementación de un gran número de &lt;a href="https://es.wikipedia.org/wiki/Algoritmo"&gt;algoritmos&lt;/a&gt; de aprendizaje. La podemos utilizar para &lt;a href='http://scikit-learn.org/stable/supervised_learning.html#supervised-learning' target='_blank'&gt;clasificaciones&lt;/a&gt;, &lt;a href='http://scikit-learn.org/stable/modules/feature_extraction.html#feature-extractionfeature' target='_blank'&gt;extraccion de características&lt;/a&gt;, &lt;a href='http://scikit-learn.org/stable/supervised_learning.html#supervised-learning' target='_blank'&gt;regresiones&lt;/a&gt;, &lt;a href='http://scikit-learn.org/stable/modules/clustering.html#clustering' target='_blank'&gt;agrupaciones&lt;/a&gt;, &lt;a href='http://scikit-learn.org/stable/modules/decomposition.html#decompositions' target='_blank'&gt;reducción de dimensiones&lt;/a&gt;, &lt;a href='http://scikit-learn.org/stable/model_selection.html#model-selection' target='_blank'&gt;selección de modelos&lt;/a&gt;, o &lt;a href='http://scikit-learn.org/stable/modules/preprocessing.html#preprocessing' target='_blank'&gt;preprocesamiento&lt;/a&gt;. Posee una &lt;a href="https://es.wikipedia.org/wiki/Interfaz_de_programaci%C3%B3n_de_aplicaciones"&gt;API&lt;/a&gt; que es consistente en todos los modelos y se integra muy bien con el resto de los paquetes científicos que ofrece &lt;a href="http://python.org/"&gt;Python&lt;/a&gt;. Esta librería también nos facilita las tareas de evaluación, diagnostico y &lt;a href="https://es.wikipedia.org/wiki/Validaci%C3%B3n_cruzada"&gt;validaciones cruzadas&lt;/a&gt; ya que nos proporciona varios métodos de fábrica para poder realizar estas tareas en forma muy simple.&lt;/p&gt;
&lt;h3 id="Statsmodels"&gt;Statsmodels&lt;a class="anchor-link" href="#Statsmodels"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;&lt;a href="http://statsmodels.sourceforge.net/"&gt;Statsmodels&lt;/a&gt; es otra gran librería que hace foco en modelos estadísticos y se utiliza principalmente para análisis predictivos y exploratorios. Al igual que &lt;a href="http://scikit-learn.org/stable/"&gt;Scikit-learn&lt;/a&gt;, también se integra muy bien con el resto de los paquetes cientificos de &lt;a href="http://python.org/"&gt;Python&lt;/a&gt;. Si deseamos ajustar modelos lineales, hacer una análisis estadístico, o tal vez un poco de modelado predictivo, entonces &lt;a href="http://statsmodels.sourceforge.net/"&gt;Statsmodels&lt;/a&gt; es la librería ideal. Las pruebas estadísticas que ofrece son bastante amplias y abarcan tareas de validación para la mayoría de los casos.&lt;/p&gt;
&lt;h3 id="PyMC"&gt;PyMC&lt;a class="anchor-link" href="#PyMC"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;&lt;a href="http://pymc-devs.github.io/pymc/index.html"&gt;pyMC&lt;/a&gt; es un módulo de &lt;a href="http://python.org/"&gt;Python&lt;/a&gt; que implementa modelos estadísticos bayesianos, incluyendo la &lt;a href="https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo"&gt;cadena de Markov Monte Carlo(MCMC)&lt;/a&gt;. &lt;a href="http://pymc-devs.github.io/pymc/index.html"&gt;pyMC&lt;/a&gt;  ofrece funcionalidades para hacer el análisis bayesiano lo mas simple posible. Incluye los modelos &lt;a href="https://es.wikipedia.org/wiki/Inferencia_bayesiana"&gt;bayesianos&lt;/a&gt;,  distribuciones estadísticas y herramientas de diagnostico  para la covarianza de los modelos. Si queremos realizar un análisis &lt;a href="https://es.wikipedia.org/wiki/Inferencia_bayesiana"&gt;bayesiano&lt;/a&gt; esta es sin duda la librería a utilizar.&lt;/p&gt;
&lt;h3 id="NTLK"&gt;NTLK&lt;a class="anchor-link" href="#NTLK"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;&lt;a href="http://www.nltk.org/"&gt;NLTK&lt;/a&gt; es la librería líder para el procesamiento del lenguaje natural o &lt;a href="https://es.wikipedia.org/wiki/Procesamiento_de_lenguajes_naturales"&gt;NLP&lt;/a&gt; por sus siglas en inglés. Proporciona interfaces fáciles de usar a más de 50 cuerpos y recursos léxicos, como &lt;a href="https://wordnet.princeton.edu/"&gt;WordNet&lt;/a&gt;, junto con un conjunto de bibliotecas de procesamiento de texto para la clasificación, tokenización, el etiquetado, el análisis y el razonamiento semántico.&lt;/p&gt;
&lt;p&gt;Obviamente, aquí solo estoy listando unas pocas de las muchas librerías que existen en &lt;a href="http://python.org/"&gt;Python&lt;/a&gt; para trabajar con problemas de &lt;a href="https://es.wikipedia.org/wiki/Aprendizaje_autom%C3%A1tico"&gt;Machine Learning&lt;/a&gt;, los invito a realizar su propia investigación sobre el tema.&lt;/p&gt;

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&lt;h2 id="Algoritmos-m&amp;#225;s-utilizados"&gt;Algoritmos m&amp;#225;s utilizados&lt;a class="anchor-link" href="#Algoritmos-m&amp;#225;s-utilizados"&gt;&amp;#182;&lt;/a&gt;&lt;/h2&gt;&lt;p&gt;Los &lt;a href="https://es.wikipedia.org/wiki/Algoritmo"&gt;algoritmos&lt;/a&gt;  que más se suelen utilizar en los problemas de &lt;a href="https://es.wikipedia.org/wiki/Aprendizaje_autom%C3%A1tico"&gt;Machine Learning&lt;/a&gt; son los siguientes:&lt;/p&gt;
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&lt;li&gt;&lt;a href="https://es.wikipedia.org/wiki/Regresi%C3%B3n_lineal"&gt;Regresión Lineal&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://es.wikipedia.org/wiki/Regresi%C3%B3n_log%C3%ADstica"&gt;Regresión Logística&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://es.wikipedia.org/wiki/%C3%81rbol_de_decisi%C3%B3n"&gt;Arboles de Decision&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://es.wikipedia.org/wiki/Random_forest"&gt;Random Forest&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://es.wikipedia.org/wiki/M%C3%A1quinas_de_vectores_de_soporte"&gt;SVM&lt;/a&gt; o Máquinas de vectores de soporte.&lt;/li&gt;
&lt;li&gt;&lt;a href="https://es.wikipedia.org/wiki/K-vecinos_m%C3%A1s_cercanos"&gt;KNN&lt;/a&gt; o K vecinos más cercanos.&lt;/li&gt;
&lt;li&gt;&lt;a href="https://es.wikipedia.org/wiki/K-means"&gt;K-means&lt;/a&gt;&lt;/li&gt;
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&lt;p&gt;Todos ellos se pueden aplicar a casi cualquier problema de datos y obviamente estan todos implementados por la excelente librería de &lt;a href="http://python.org/"&gt;Python&lt;/a&gt;, &lt;a href="http://scikit-learn.org/stable/"&gt;Scikit-learn&lt;/a&gt;. Veamos algunos ejemplos de ellos.&lt;/p&gt;

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&lt;h3 id="Regresi&amp;#243;n-Lineal"&gt;Regresi&amp;#243;n Lineal&lt;a class="anchor-link" href="#Regresi&amp;#243;n-Lineal"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;Se utiliza para estimar los valores reales (costo de las viviendas, el número de llamadas, ventas totales, etc.) basados en variables continuas. La idea es tratar de establecer la relación entre las variables independientes y dependientes por medio de ajustar una mejor línea recta con respecto a los puntos. Esta línea de mejor ajuste se conoce como línea de regresión y esta representada por la siguiente ecuación lineal:&lt;/p&gt;
$$Y = \beta_{0} + \beta_{1}X_{1} + \beta_{2}X_{2} + ... + \beta_{n}X_{n}$$&lt;p&gt;Veamos un pequeño ejemplo de como se implementa en &lt;a href="http://python.org/"&gt;Python&lt;/a&gt;. En este ejemplo voy a utilizar el dataset Boston que ya viene junto con &lt;a href="http://scikit-learn.org/stable/"&gt;Scikit-learn&lt;/a&gt; y es ideal para practicar con &lt;a href="https://es.wikipedia.org/wiki/Regresi%C3%B3n_lineal"&gt;Regresiones Lineales&lt;/a&gt;; el mismo contiene precios de casas de varias áreas de la ciudad de Boston.&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# graficos embebidos&lt;/span&gt;
&lt;span class="o"&gt;%&lt;/span&gt;&lt;span class="k"&gt;matplotlib&lt;/span&gt; inline
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# importando pandas, numpy y matplotlib&lt;/span&gt;
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;pandas&lt;/span&gt; &lt;span class="k"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;pd&lt;/span&gt;
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;numpy&lt;/span&gt; &lt;span class="k"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;np&lt;/span&gt;
&lt;span class="kn"&gt;import&lt;/span&gt; &lt;span class="nn"&gt;matplotlib.pyplot&lt;/span&gt; &lt;span class="k"&gt;as&lt;/span&gt; &lt;span class="nn"&gt;plt&lt;/span&gt;

&lt;span class="c1"&gt;# importando los datasets de sklearn&lt;/span&gt;
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;sklearn&lt;/span&gt; &lt;span class="k"&gt;import&lt;/span&gt; &lt;span class="n"&gt;datasets&lt;/span&gt;

&lt;span class="n"&gt;boston&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;datasets&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;load_boston&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;span class="n"&gt;boston_df&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;DataFrame&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;boston&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;data&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;columns&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;boston&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;feature_names&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;boston_df&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;TARGET&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;boston&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;target&lt;/span&gt;
&lt;span class="n"&gt;boston_df&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;head&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt; &lt;span class="c1"&gt;# estructura de nuestro dataset.&lt;/span&gt;
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&lt;table border="1" class="dataframe"&gt;
  &lt;thead&gt;
    &lt;tr style="text-align: right;"&gt;
      &lt;th&gt;&lt;/th&gt;
      &lt;th&gt;CRIM&lt;/th&gt;
      &lt;th&gt;ZN&lt;/th&gt;
      &lt;th&gt;INDUS&lt;/th&gt;
      &lt;th&gt;CHAS&lt;/th&gt;
      &lt;th&gt;NOX&lt;/th&gt;
      &lt;th&gt;RM&lt;/th&gt;
      &lt;th&gt;AGE&lt;/th&gt;
      &lt;th&gt;DIS&lt;/th&gt;
      &lt;th&gt;RAD&lt;/th&gt;
      &lt;th&gt;TAX&lt;/th&gt;
      &lt;th&gt;PTRATIO&lt;/th&gt;
      &lt;th&gt;B&lt;/th&gt;
      &lt;th&gt;LSTAT&lt;/th&gt;
      &lt;th&gt;TARGET&lt;/th&gt;
    &lt;/tr&gt;
  &lt;/thead&gt;
  &lt;tbody&gt;
    &lt;tr&gt;
      &lt;th&gt;0&lt;/th&gt;
      &lt;td&gt;0.00632&lt;/td&gt;
      &lt;td&gt;18.0&lt;/td&gt;
      &lt;td&gt;2.31&lt;/td&gt;
      &lt;td&gt;0.0&lt;/td&gt;
      &lt;td&gt;0.538&lt;/td&gt;
      &lt;td&gt;6.575&lt;/td&gt;
      &lt;td&gt;65.2&lt;/td&gt;
      &lt;td&gt;4.0900&lt;/td&gt;
      &lt;td&gt;1.0&lt;/td&gt;
      &lt;td&gt;296.0&lt;/td&gt;
      &lt;td&gt;15.3&lt;/td&gt;
      &lt;td&gt;396.90&lt;/td&gt;
      &lt;td&gt;4.98&lt;/td&gt;
      &lt;td&gt;24.0&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;1&lt;/th&gt;
      &lt;td&gt;0.02731&lt;/td&gt;
      &lt;td&gt;0.0&lt;/td&gt;
      &lt;td&gt;7.07&lt;/td&gt;
      &lt;td&gt;0.0&lt;/td&gt;
      &lt;td&gt;0.469&lt;/td&gt;
      &lt;td&gt;6.421&lt;/td&gt;
      &lt;td&gt;78.9&lt;/td&gt;
      &lt;td&gt;4.9671&lt;/td&gt;
      &lt;td&gt;2.0&lt;/td&gt;
      &lt;td&gt;242.0&lt;/td&gt;
      &lt;td&gt;17.8&lt;/td&gt;
      &lt;td&gt;396.90&lt;/td&gt;
      &lt;td&gt;9.14&lt;/td&gt;
      &lt;td&gt;21.6&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;2&lt;/th&gt;
      &lt;td&gt;0.02729&lt;/td&gt;
      &lt;td&gt;0.0&lt;/td&gt;
      &lt;td&gt;7.07&lt;/td&gt;
      &lt;td&gt;0.0&lt;/td&gt;
      &lt;td&gt;0.469&lt;/td&gt;
      &lt;td&gt;7.185&lt;/td&gt;
      &lt;td&gt;61.1&lt;/td&gt;
      &lt;td&gt;4.9671&lt;/td&gt;
      &lt;td&gt;2.0&lt;/td&gt;
      &lt;td&gt;242.0&lt;/td&gt;
      &lt;td&gt;17.8&lt;/td&gt;
      &lt;td&gt;392.83&lt;/td&gt;
      &lt;td&gt;4.03&lt;/td&gt;
      &lt;td&gt;34.7&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;3&lt;/th&gt;
      &lt;td&gt;0.03237&lt;/td&gt;
      &lt;td&gt;0.0&lt;/td&gt;
      &lt;td&gt;2.18&lt;/td&gt;
      &lt;td&gt;0.0&lt;/td&gt;
      &lt;td&gt;0.458&lt;/td&gt;
      &lt;td&gt;6.998&lt;/td&gt;
      &lt;td&gt;45.8&lt;/td&gt;
      &lt;td&gt;6.0622&lt;/td&gt;
      &lt;td&gt;3.0&lt;/td&gt;
      &lt;td&gt;222.0&lt;/td&gt;
      &lt;td&gt;18.7&lt;/td&gt;
      &lt;td&gt;394.63&lt;/td&gt;
      &lt;td&gt;2.94&lt;/td&gt;
      &lt;td&gt;33.4&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;4&lt;/th&gt;
      &lt;td&gt;0.06905&lt;/td&gt;
      &lt;td&gt;0.0&lt;/td&gt;
      &lt;td&gt;2.18&lt;/td&gt;
      &lt;td&gt;0.0&lt;/td&gt;
      &lt;td&gt;0.458&lt;/td&gt;
      &lt;td&gt;7.147&lt;/td&gt;
      &lt;td&gt;54.2&lt;/td&gt;
      &lt;td&gt;6.0622&lt;/td&gt;
      &lt;td&gt;3.0&lt;/td&gt;
      &lt;td&gt;222.0&lt;/td&gt;
      &lt;td&gt;18.7&lt;/td&gt;
      &lt;td&gt;396.90&lt;/td&gt;
      &lt;td&gt;5.33&lt;/td&gt;
      &lt;td&gt;36.2&lt;/td&gt;
    &lt;/tr&gt;
  &lt;/tbody&gt;
&lt;/table&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# importando el modelo de regresión lineal&lt;/span&gt;
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;sklearn.linear_model&lt;/span&gt; &lt;span class="k"&gt;import&lt;/span&gt; &lt;span class="n"&gt;LinearRegression&lt;/span&gt;

&lt;span class="n"&gt;rl&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;LinearRegression&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt; &lt;span class="c1"&gt;# Creando el modelo.&lt;/span&gt;
&lt;span class="n"&gt;rl&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;fit&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;boston&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;data&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;boston&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;target&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="c1"&gt;# ajustando el modelo&lt;/span&gt;
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&lt;pre class="ipynb"&gt;LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)&lt;/pre&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Lista de coeficientes B para cada X&lt;/span&gt;
&lt;span class="nb"&gt;list&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="nb"&gt;zip&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;boston&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;feature_names&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;rl&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;coef_&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt;
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&lt;pre class="ipynb"&gt;[(&amp;#39;CRIM&amp;#39;, -0.10717055656035465),
 (&amp;#39;ZN&amp;#39;, 0.046395219529796909),
 (&amp;#39;INDUS&amp;#39;, 0.020860239532179539),
 (&amp;#39;CHAS&amp;#39;, 2.6885613993179454),
 (&amp;#39;NOX&amp;#39;, -17.795758660309225),
 (&amp;#39;RM&amp;#39;, 3.804752460258007),
 (&amp;#39;AGE&amp;#39;, 0.0007510617033251836),
 (&amp;#39;DIS&amp;#39;, -1.4757587965198111),
 (&amp;#39;RAD&amp;#39;, 0.30565503833909574),
 (&amp;#39;TAX&amp;#39;, -0.012329346305270286),
 (&amp;#39;PTRATIO&amp;#39;, -0.95346355469055721),
 (&amp;#39;B&amp;#39;, 0.0093925127221896297),
 (&amp;#39;LSTAT&amp;#39;, -0.52546663290078854)]&lt;/pre&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# haciendo las predicciones&lt;/span&gt;
&lt;span class="n"&gt;predicciones&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;rl&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;predict&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;boston&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;data&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;predicciones_df&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;pd&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;DataFrame&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;predicciones&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;columns&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Pred&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;])&lt;/span&gt;
&lt;span class="n"&gt;predicciones_df&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;head&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt; &lt;span class="c1"&gt;# predicciones de las primeras 5 lineas&lt;/span&gt;
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  &lt;thead&gt;
    &lt;tr style="text-align: right;"&gt;
      &lt;th&gt;&lt;/th&gt;
      &lt;th&gt;Pred&lt;/th&gt;
    &lt;/tr&gt;
  &lt;/thead&gt;
  &lt;tbody&gt;
    &lt;tr&gt;
      &lt;th&gt;0&lt;/th&gt;
      &lt;td&gt;30.008213&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;1&lt;/th&gt;
      &lt;td&gt;25.029861&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;2&lt;/th&gt;
      &lt;td&gt;30.570232&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;3&lt;/th&gt;
      &lt;td&gt;28.608141&lt;/td&gt;
    &lt;/tr&gt;
    &lt;tr&gt;
      &lt;th&gt;4&lt;/th&gt;
      &lt;td&gt;27.942882&lt;/td&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Calculando el desvio&lt;/span&gt;
&lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;mean&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;boston&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;target&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt; &lt;span class="n"&gt;predicciones&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
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&lt;pre class="ipynb"&gt;-2.2467754490437951e-15&lt;/pre&gt;
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&lt;p&gt;Como podemos ver, el desvío del modelo es pequeño, por lo que sus resultados para este ejemplo son bastante confiables.&lt;/p&gt;

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&lt;h3 id="Regresi&amp;#243;n-Log&amp;#237;stica"&gt;Regresi&amp;#243;n Log&amp;#237;stica&lt;a class="anchor-link" href="#Regresi&amp;#243;n-Log&amp;#237;stica"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;Los modelos lineales, también pueden ser utilizados para clasificaciones; es decir, que primero ajustamos el modelo lineal a la probabilidad de que una cierta clase o categoría ocurra y, a luego, utilizamos una función para crear un umbral en el cual especificamos el resultado de una de estas clases o categorías. La función que utiliza este modelo, no es ni más ni menos que la función logística.&lt;/p&gt;
$$f(x) = \frac{1}{1 + e^{-1}}$$&lt;p&gt;Veamos, aquí también un pequeño ejemplo en &lt;a href="http://python.org/"&gt;Python&lt;/a&gt;.&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Creando un dataset de ejemplo &lt;/span&gt;
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;sklearn.datasets&lt;/span&gt; &lt;span class="k"&gt;import&lt;/span&gt; &lt;span class="n"&gt;make_classification&lt;/span&gt;
&lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;make_classification&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;n_samples&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;1000&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;n_features&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;4&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Importando el modelo&lt;/span&gt;
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;sklearn.linear_model&lt;/span&gt; &lt;span class="k"&gt;import&lt;/span&gt; &lt;span class="n"&gt;LogisticRegression&lt;/span&gt;

&lt;span class="n"&gt;rlog&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;LogisticRegression&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt; &lt;span class="c1"&gt;# Creando el modelo&lt;/span&gt;

&lt;span class="c1"&gt;# Dividiendo el dataset en entrenamiento y evaluacion&lt;/span&gt;
&lt;span class="n"&gt;X_entrenamiento&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="p"&gt;[:&lt;/span&gt;&lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="mi"&gt;200&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;
&lt;span class="n"&gt;X_evaluacion&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="mi"&gt;200&lt;/span&gt;&lt;span class="p"&gt;:]&lt;/span&gt;
&lt;span class="n"&gt;y_entrenamiento&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;y&lt;/span&gt;&lt;span class="p"&gt;[:&lt;/span&gt;&lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="mi"&gt;200&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;
&lt;span class="n"&gt;y_evaluacion&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;y&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="mi"&gt;200&lt;/span&gt;&lt;span class="p"&gt;:]&lt;/span&gt;

&lt;span class="n"&gt;rlog&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;fit&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;X_entrenamiento&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y_entrenamiento&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="c1"&gt;#ajustando el modelo&lt;/span&gt;

&lt;span class="c1"&gt;# Realizando las predicciones&lt;/span&gt;
&lt;span class="n"&gt;y_predic_entrenamiento&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;rlog&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;predict&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;X_entrenamiento&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; 
&lt;span class="n"&gt;y_predic_evaluacion&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;rlog&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;predict&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;X_evaluacion&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Verificando la exactitud del modelo&lt;/span&gt;
&lt;span class="n"&gt;entrenamiento&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;y_predic_entrenamiento&lt;/span&gt; &lt;span class="o"&gt;==&lt;/span&gt; &lt;span class="n"&gt;y_entrenamiento&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;sum&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;astype&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="nb"&gt;float&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="o"&gt;/&lt;/span&gt; &lt;span class="n"&gt;y_entrenamiento&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;shape&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;
&lt;span class="nb"&gt;print&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;sobre datos de entrenamiento: &lt;/span&gt;&lt;span class="si"&gt;{0:.2f}&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;format&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;entrenamiento&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt;
&lt;span class="n"&gt;evaluacion&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;y_predic_evaluacion&lt;/span&gt; &lt;span class="o"&gt;==&lt;/span&gt; &lt;span class="n"&gt;y_evaluacion&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;sum&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;astype&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="nb"&gt;float&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="o"&gt;/&lt;/span&gt; &lt;span class="n"&gt;y_evaluacion&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;shape&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;
&lt;span class="nb"&gt;print&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;sobre datos de evaluación: &lt;/span&gt;&lt;span class="si"&gt;{0:.2f}&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;format&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;evaluacion&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt;
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&lt;pre class="ipynb"&gt;sobre datos de entrenamiento: 0.95
sobre datos de evaluación: 0.94
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&lt;p&gt;Como podemos ver en este ejemplo también nuestro modelo tiene bastante precisión clasificando las categorías de nuestro dataset.&lt;/p&gt;

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&lt;h3 id="Arboles-de-decisi&amp;#243;n"&gt;Arboles de decisi&amp;#243;n&lt;a class="anchor-link" href="#Arboles-de-decisi&amp;#243;n"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;Los &lt;a href="https://es.wikipedia.org/wiki/%C3%81rbol_de_decisi%C3%B3n"&gt;Arboles de Decision&lt;/a&gt; son diagramas con construcciones lógicas, muy similares a los sistemas de predicción basados en reglas, que sirven para representar y categorizar una serie de condiciones que ocurren de forma sucesiva, para la resolución de un problema.
Los &lt;a href="https://es.wikipedia.org/wiki/%C3%81rbol_de_decisi%C3%B3n"&gt;Arboles de Decision&lt;/a&gt; están compuestos por nodos interiores, nodos terminales y ramas que emanan de los nodos interiores. Cada nodo interior en el árbol contiene una prueba de un atributo, y cada rama representa un valor distinto del atributo. Siguiendo las ramas desde el nodo raíz hacia abajo, cada ruta finalmente termina en un nodo terminal creando una segmentación de los datos. Veamos aquí también un pequeño ejemplo en &lt;a href="http://python.org/"&gt;Python&lt;/a&gt;.&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Creando un dataset de ejemplo&lt;/span&gt;
&lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;datasets&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;make_classification&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mi"&gt;1000&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;20&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;n_informative&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;3&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="c1"&gt;# Importando el arbol de decisión&lt;/span&gt;
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;sklearn.tree&lt;/span&gt; &lt;span class="k"&gt;import&lt;/span&gt; &lt;span class="n"&gt;DecisionTreeClassifier&lt;/span&gt;
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;sklearn&lt;/span&gt; &lt;span class="k"&gt;import&lt;/span&gt; &lt;span class="n"&gt;tree&lt;/span&gt;

&lt;span class="n"&gt;ad&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;DecisionTreeClassifier&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;criterion&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;entropy&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;max_depth&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;5&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="c1"&gt;# Creando el modelo&lt;/span&gt;
&lt;span class="n"&gt;ad&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;fit&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="c1"&gt;# Ajustando el modelo&lt;/span&gt;

&lt;span class="c1"&gt;#generando archivo para graficar el arbol&lt;/span&gt;
&lt;span class="k"&gt;with&lt;/span&gt; &lt;span class="nb"&gt;open&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;mi_arbol.dot&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;w&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="k"&gt;as&lt;/span&gt; &lt;span class="n"&gt;archivo_dot&lt;/span&gt;&lt;span class="p"&gt;:&lt;/span&gt;
    &lt;span class="n"&gt;tree&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;export_graphviz&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;ad&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;out_file&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;archivo_dot&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# utilizando el lenguaje dot para graficar el arbol.&lt;/span&gt;
&lt;span class="o"&gt;!&lt;/span&gt;dot -Tjpeg mi_arbol.dot -o arbol_decision.jpeg
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&lt;p&gt;Luego de usar el lenguaje &lt;a href="http://www.graphviz.org/content/dot-language"&gt;dot&lt;/a&gt; para convertir nuestro arbol a formato jpeg, ya podemos ver la imagen del mismo.&lt;/p&gt;
&lt;p&gt;&lt;img alt="arbol de decision" title="arbol de decision" src="http://relopezbriega.github.io/images/arbol_decision.jpeg"&gt;&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# verificando la precisión&lt;/span&gt;
&lt;span class="nb"&gt;print&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;precisión del modelo: &lt;/span&gt;&lt;span class="si"&gt;{0: .2f}&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;format&lt;/span&gt;&lt;span class="p"&gt;((&lt;/span&gt;&lt;span class="n"&gt;y&lt;/span&gt; &lt;span class="o"&gt;==&lt;/span&gt; &lt;span class="n"&gt;ad&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;predict&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;mean&lt;/span&gt;&lt;span class="p"&gt;()))&lt;/span&gt;
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&lt;pre class="ipynb"&gt;precisión del modelo:  0.94
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&lt;p&gt;En este ejemplo, nuestro árbol tiene una precisión del 89%. Tener en cuenta que los &lt;a href="https://es.wikipedia.org/wiki/%C3%81rbol_de_decisi%C3%B3n"&gt;Arboles de Decision&lt;/a&gt; tienen tendencia a &lt;em&gt;sobreentrenar&lt;/em&gt; los datos.&lt;/p&gt;

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&lt;h3 id="Random-Forest"&gt;Random Forest&lt;a class="anchor-link" href="#Random-Forest"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;En lugar de utilizar solo un arbol para decidir, ¿por qué no utilizar todo un bosque?!!. Esta es la idea central detrás del &lt;a href="https://es.wikipedia.org/wiki/Algoritmo"&gt;algoritmo&lt;/a&gt; de &lt;a href="https://es.wikipedia.org/wiki/Random_forest"&gt;Random Forest&lt;/a&gt;. Tarbaja construyendo una gran cantidad de &lt;a href="https://es.wikipedia.org/wiki/%C3%81rbol_de_decisi%C3%B3n"&gt;arboles de decision&lt;/a&gt; muy poco profundos, y luego toma la clase que
cada árbol eligió. Esta idea es muy poderosa en &lt;a href="https://es.wikipedia.org/wiki/Aprendizaje_autom%C3%A1tico"&gt;Machine Learning&lt;/a&gt;. Si tenemos en cuenta que un sencillo clasificador entrenado podría tener sólo el 60 por ciento de precisión, podemos entrenar un montón de clasificadores que sean por lo general acertados y luego podemos utilizar la sabiduría de todos los aprendices juntos.
Con &lt;a href="http://python.org/"&gt;Python&lt;/a&gt; los podemos utilizar de la siguiente manera:&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Creando un dataset de ejemplo&lt;/span&gt;
&lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;datasets&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;make_classification&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mi"&gt;1000&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="c1"&gt;# Importando el random forest&lt;/span&gt;
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;sklearn.ensemble&lt;/span&gt; &lt;span class="k"&gt;import&lt;/span&gt; &lt;span class="n"&gt;RandomForestClassifier&lt;/span&gt;

&lt;span class="n"&gt;rf&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;RandomForestClassifier&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt; &lt;span class="c1"&gt;# Creando el modelo&lt;/span&gt;
&lt;span class="n"&gt;rf&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;fit&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="c1"&gt;# Ajustando el modelo&lt;/span&gt;

&lt;span class="c1"&gt;# verificando la precisión&lt;/span&gt;
&lt;span class="nb"&gt;print&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;precisión del modelo: &lt;/span&gt;&lt;span class="si"&gt;{0: .2f}&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;format&lt;/span&gt;&lt;span class="p"&gt;((&lt;/span&gt;&lt;span class="n"&gt;y&lt;/span&gt; &lt;span class="o"&gt;==&lt;/span&gt; &lt;span class="n"&gt;rf&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;predict&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;mean&lt;/span&gt;&lt;span class="p"&gt;()))&lt;/span&gt;
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&lt;pre class="ipynb"&gt;precisión del modelo:  1.00
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&lt;h3 id="SVM-o-M&amp;#225;quinas-de-vectores-de-soporte"&gt;SVM o M&amp;#225;quinas de vectores de soporte&lt;a class="anchor-link" href="#SVM-o-M&amp;#225;quinas-de-vectores-de-soporte"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;La idea detrás de &lt;a href="https://es.wikipedia.org/wiki/M%C3%A1quinas_de_vectores_de_soporte"&gt;SVM&lt;/a&gt; es encontrar un plano que separe los grupos dentro de los datos de la mejor forma posible. Aquí, la separación significa que la elección
del plano maximiza el margen entre los puntos más cercanos en el plano; éstos puntos se denominan vectores de soporte. Pasemos al ejemplo.&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# importanto SVM&lt;/span&gt;
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;sklearn&lt;/span&gt; &lt;span class="k"&gt;import&lt;/span&gt; &lt;span class="n"&gt;svm&lt;/span&gt;

&lt;span class="c1"&gt;# importando el dataset iris&lt;/span&gt;
&lt;span class="n"&gt;iris&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;datasets&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;load_iris&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;span class="n"&gt;X&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;iris&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;data&lt;/span&gt;&lt;span class="p"&gt;[:,&lt;/span&gt; &lt;span class="p"&gt;:&lt;/span&gt;&lt;span class="mi"&gt;2&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;  &lt;span class="c1"&gt;# solo tomamos las primeras 2 características&lt;/span&gt;
&lt;span class="n"&gt;y&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;iris&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;target&lt;/span&gt;

&lt;span class="n"&gt;h&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="mi"&gt;02&lt;/span&gt;  &lt;span class="c1"&gt;# tamaño de la malla del grafico&lt;/span&gt;

&lt;span class="c1"&gt;# Creando el SVM con sus diferentes métodos&lt;/span&gt;
&lt;span class="n"&gt;C&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="mf"&gt;1.0&lt;/span&gt;  &lt;span class="c1"&gt;# parametro de regulacion SVM &lt;/span&gt;
&lt;span class="n"&gt;svc&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;svm&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;SVC&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;kernel&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;linear&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;C&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;C&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;fit&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;rbf_svc&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;svm&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;SVC&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;kernel&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;rbf&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;gamma&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mf"&gt;0.7&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;C&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;C&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;fit&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;poly_svc&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;svm&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;SVC&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;kernel&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;poly&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;degree&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;3&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;C&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;C&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;fit&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;lin_svc&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;svm&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;LinearSVC&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;C&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;C&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;fit&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="c1"&gt;# crear el area para graficar&lt;/span&gt;
&lt;span class="n"&gt;x_min&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;x_max&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="p"&gt;[:,&lt;/span&gt; &lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;min&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt; &lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="p"&gt;[:,&lt;/span&gt; &lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;max&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt; &lt;span class="o"&gt;+&lt;/span&gt; &lt;span class="mi"&gt;1&lt;/span&gt;
&lt;span class="n"&gt;y_min&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y_max&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="p"&gt;[:,&lt;/span&gt; &lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;min&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt; &lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="p"&gt;[:,&lt;/span&gt; &lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;max&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt; &lt;span class="o"&gt;+&lt;/span&gt; &lt;span class="mi"&gt;1&lt;/span&gt;
&lt;span class="n"&gt;xx&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;yy&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;meshgrid&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;arange&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;x_min&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;x_max&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;h&lt;/span&gt;&lt;span class="p"&gt;),&lt;/span&gt;
                     &lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;arange&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;y_min&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y_max&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;h&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt;

&lt;span class="c1"&gt;# titulos de los graficos&lt;/span&gt;
&lt;span class="n"&gt;titles&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;SVC con el motor lineal&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
          &lt;span class="s1"&gt;&amp;#39;LinearSVC&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
          &lt;span class="s1"&gt;&amp;#39;SVC con el motor RBF&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
          &lt;span class="s1"&gt;&amp;#39;SVC con el motor polinomial&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;


&lt;span class="k"&gt;for&lt;/span&gt; &lt;span class="n"&gt;i&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;clf&lt;/span&gt; &lt;span class="ow"&gt;in&lt;/span&gt; &lt;span class="nb"&gt;enumerate&lt;/span&gt;&lt;span class="p"&gt;((&lt;/span&gt;&lt;span class="n"&gt;svc&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;lin_svc&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;rbf_svc&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;poly_svc&lt;/span&gt;&lt;span class="p"&gt;)):&lt;/span&gt;
    &lt;span class="c1"&gt;# Realizando el gráfico, se le asigna un color a cada punto&lt;/span&gt;
    &lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;subplot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mi"&gt;2&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;2&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;i&lt;/span&gt; &lt;span class="o"&gt;+&lt;/span&gt; &lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
    &lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;subplots_adjust&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;wspace&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mf"&gt;0.4&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;hspace&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mf"&gt;0.4&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

    &lt;span class="n"&gt;Z&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;clf&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;predict&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;c_&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;xx&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;ravel&lt;/span&gt;&lt;span class="p"&gt;(),&lt;/span&gt; &lt;span class="n"&gt;yy&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;ravel&lt;/span&gt;&lt;span class="p"&gt;()])&lt;/span&gt;

    &lt;span class="n"&gt;Z&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;Z&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;reshape&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;xx&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;shape&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
    &lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;contourf&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;xx&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;yy&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;Z&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;cmap&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;cm&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;Paired&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;alpha&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mf"&gt;0.8&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

    &lt;span class="c1"&gt;# Graficando tambien los puntos de datos&lt;/span&gt;
    &lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;scatter&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="p"&gt;[:,&lt;/span&gt; &lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="p"&gt;[:,&lt;/span&gt; &lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="n"&gt;c&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;y&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;cmap&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;cm&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;Paired&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
    &lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;xlabel&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;largo del petalo&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
    &lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;ylabel&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;ancho del petalo&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
    &lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;xlim&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;xx&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;min&lt;/span&gt;&lt;span class="p"&gt;(),&lt;/span&gt; &lt;span class="n"&gt;xx&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;max&lt;/span&gt;&lt;span class="p"&gt;())&lt;/span&gt;
    &lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;ylim&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;yy&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;min&lt;/span&gt;&lt;span class="p"&gt;(),&lt;/span&gt; &lt;span class="n"&gt;yy&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;max&lt;/span&gt;&lt;span class="p"&gt;())&lt;/span&gt;
    &lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;xticks&lt;/span&gt;&lt;span class="p"&gt;(())&lt;/span&gt;
    &lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;yticks&lt;/span&gt;&lt;span class="p"&gt;(())&lt;/span&gt;
    &lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;title&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;titles&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;i&lt;/span&gt;&lt;span class="p"&gt;])&lt;/span&gt;

&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;show&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;div class="output_wrapper"&gt;
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&lt;h3 id="KNN-o-k-vecinos-m&amp;#225;s-cercanos"&gt;KNN o k vecinos m&amp;#225;s cercanos&lt;a class="anchor-link" href="#KNN-o-k-vecinos-m&amp;#225;s-cercanos"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;Este es un método de clasificación no paramétrico, que estima el valor de la probabilidad a posteriori de que un elemento $x$ pertenezca a una clase en particular a partir de la información proporcionada por el conjunto de prototipos.
La regresión &lt;a href="https://es.wikipedia.org/wiki/K-vecinos_m%C3%A1s_cercanos"&gt;KNN&lt;/a&gt; se calcula simplemente tomando el promedio del punto k más cercano al punto que se está probando.&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Creando el dataset iris&lt;/span&gt;
&lt;span class="n"&gt;iris&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;datasets&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;load_iris&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;span class="n"&gt;X&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;iris&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;data&lt;/span&gt;
&lt;span class="n"&gt;y&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;iris&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;target&lt;/span&gt;
&lt;span class="n"&gt;iris&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;feature_names&lt;/span&gt;
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&lt;pre class="ipynb"&gt;[&amp;#39;sepal length (cm)&amp;#39;,
 &amp;#39;sepal width (cm)&amp;#39;,
 &amp;#39;petal length (cm)&amp;#39;,
 &amp;#39;petal width (cm)&amp;#39;]&lt;/pre&gt;
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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# importando KNN &lt;/span&gt;
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;sklearn.neighbors&lt;/span&gt; &lt;span class="k"&gt;import&lt;/span&gt; &lt;span class="n"&gt;KNeighborsRegressor&lt;/span&gt;

&lt;span class="n"&gt;knnr&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;KNeighborsRegressor&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;n_neighbors&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;10&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="c1"&gt;# Creando el modelo con 10 vecinos&lt;/span&gt;
&lt;span class="n"&gt;knnr&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;fit&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="c1"&gt;# Ajustando el modelo&lt;/span&gt;

&lt;span class="c1"&gt;# Verificando el error medio del modelo&lt;/span&gt;
&lt;span class="nb"&gt;print&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;El error medio del modelo es: &lt;/span&gt;&lt;span class="si"&gt;{:.2f}&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;format&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;np&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;power&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;y&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt; &lt;span class="n"&gt;knnr&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;predict&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="p"&gt;),&lt;/span&gt;
&lt;span class="mi"&gt;2&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;mean&lt;/span&gt;&lt;span class="p"&gt;()))&lt;/span&gt;
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&lt;pre class="ipynb"&gt;El error medio del modelo es: 0.02
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&lt;h3 id="K-means"&gt;K-means&lt;a class="anchor-link" href="#K-means"&gt;&amp;#182;&lt;/a&gt;&lt;/h3&gt;&lt;p&gt;&lt;a href="https://es.wikipedia.org/wiki/K-means"&gt;K-means&lt;/a&gt; es probablemente uno de los algoritmos de agrupamiento más conocidos y, en un sentido más amplio, una de las técnicas de aprendizaje no supervisado más conocidas.
&lt;a href="https://es.wikipedia.org/wiki/K-means"&gt;K-means&lt;/a&gt; es en realidad un &lt;a href="https://es.wikipedia.org/wiki/Algoritmo"&gt;algoritmo&lt;/a&gt; muy simple que funciona para reducir al mínimo la suma de las distancias cuadradas desde la media dentro del agrupamiento. Para hacer esto establece primero un número previamente especificado de conglomerados, K, y luego va asignando cada observación a la agrupación más cercana de acuerdo a su media. Veamos el ejemplo&lt;/p&gt;

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        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Creando el dataset&lt;/span&gt;
&lt;span class="n"&gt;grupos&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;pos_correcta&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;datasets&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;make_blobs&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mi"&gt;1000&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;centers&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;3&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
&lt;span class="n"&gt;cluster_std&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mf"&gt;1.75&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="c1"&gt;# Graficando los grupos de datos&lt;/span&gt;
&lt;span class="n"&gt;f&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;ax&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;subplots&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;figsize&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mi"&gt;7&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;5&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt;
&lt;span class="n"&gt;colores&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;r&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;g&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;b&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;

&lt;span class="k"&gt;for&lt;/span&gt; &lt;span class="n"&gt;i&lt;/span&gt; &lt;span class="ow"&gt;in&lt;/span&gt; &lt;span class="nb"&gt;range&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mi"&gt;3&lt;/span&gt;&lt;span class="p"&gt;):&lt;/span&gt;
    &lt;span class="n"&gt;p&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;grupos&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;pos_correcta&lt;/span&gt; &lt;span class="o"&gt;==&lt;/span&gt; &lt;span class="n"&gt;i&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;
    &lt;span class="n"&gt;ax&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;scatter&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;p&lt;/span&gt;&lt;span class="p"&gt;[:,&lt;/span&gt;&lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="n"&gt;p&lt;/span&gt;&lt;span class="p"&gt;[:,&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="n"&gt;c&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;colores&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;i&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt;
               &lt;span class="n"&gt;label&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;Grupo &lt;/span&gt;&lt;span class="si"&gt;{}&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;format&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;i&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt;

&lt;span class="n"&gt;ax&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_title&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;Agrupamiento perfecto&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;ax&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;legend&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;

&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;show&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
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"
&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[18]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# importando KMeans&lt;/span&gt;
&lt;span class="kn"&gt;from&lt;/span&gt; &lt;span class="nn"&gt;sklearn.cluster&lt;/span&gt; &lt;span class="k"&gt;import&lt;/span&gt; &lt;span class="n"&gt;KMeans&lt;/span&gt;

&lt;span class="c1"&gt;# Creando el modelo&lt;/span&gt;
&lt;span class="n"&gt;kmeans&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;KMeans&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;n_clusters&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;3&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;kmeans&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;fit&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;grupos&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="c1"&gt;# Ajustando el modelo&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;div class="output_wrapper"&gt;
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&lt;div class="output_area"&gt;
&lt;div class="prompt output_prompt"&gt;Out[18]:&lt;/div&gt;



&lt;div class="output_text output_subarea output_execute_result"&gt;
&lt;pre class="ipynb"&gt;KMeans(copy_x=True, init=&amp;#39;k-means++&amp;#39;, max_iter=300, n_clusters=3, n_init=10,
    n_jobs=1, precompute_distances=&amp;#39;auto&amp;#39;, random_state=None, tol=0.0001,
    verbose=0)&lt;/pre&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[19]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# verificando los centros de los grupos&lt;/span&gt;
&lt;span class="n"&gt;kmeans&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;cluster_centers_&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;div class="output_wrapper"&gt;
&lt;div class="output"&gt;


&lt;div class="output_area"&gt;
&lt;div class="prompt output_prompt"&gt;Out[19]:&lt;/div&gt;



&lt;div class="output_text output_subarea output_execute_result"&gt;
&lt;pre class="ipynb"&gt;array([[ 9.11516192,  9.17538524],
       [-4.4747179 ,  5.36904317],
       [ 6.23263898,  3.44671141]])&lt;/pre&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In&amp;nbsp;[20]:&lt;/div&gt;

&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
        &lt;div class="highlight-ipynb"&gt;&lt;pre class="ipynb"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="c1"&gt;# Graficando segun modelo&lt;/span&gt;
&lt;span class="n"&gt;f&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;ax&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;subplots&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;figsize&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mi"&gt;7&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="mi"&gt;5&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt;
&lt;span class="n"&gt;colores&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;r&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;g&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s1"&gt;&amp;#39;b&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;

&lt;span class="k"&gt;for&lt;/span&gt; &lt;span class="n"&gt;i&lt;/span&gt; &lt;span class="ow"&gt;in&lt;/span&gt; &lt;span class="nb"&gt;range&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="mi"&gt;3&lt;/span&gt;&lt;span class="p"&gt;):&lt;/span&gt;
    &lt;span class="n"&gt;p&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;grupos&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;pos_correcta&lt;/span&gt; &lt;span class="o"&gt;==&lt;/span&gt; &lt;span class="n"&gt;i&lt;/span&gt;&lt;span class="p"&gt;]&lt;/span&gt;
    &lt;span class="n"&gt;ax&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;scatter&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;p&lt;/span&gt;&lt;span class="p"&gt;[:,&lt;/span&gt;&lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="n"&gt;p&lt;/span&gt;&lt;span class="p"&gt;[:,&lt;/span&gt;&lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="n"&gt;c&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;colores&lt;/span&gt;&lt;span class="p"&gt;[&lt;/span&gt;&lt;span class="n"&gt;i&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt;
               &lt;span class="n"&gt;label&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;Grupo &lt;/span&gt;&lt;span class="si"&gt;{}&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;format&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;i&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt;

&lt;span class="n"&gt;ax&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;scatter&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;kmeans&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;cluster_centers_&lt;/span&gt;&lt;span class="p"&gt;[:,&lt;/span&gt; &lt;span class="mi"&gt;0&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; &lt;span class="n"&gt;kmeans&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;cluster_centers_&lt;/span&gt;&lt;span class="p"&gt;[:,&lt;/span&gt; &lt;span class="mi"&gt;1&lt;/span&gt;&lt;span class="p"&gt;],&lt;/span&gt; 
           &lt;span class="n"&gt;s&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="mi"&gt;100&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;color&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;black&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;label&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s1"&gt;&amp;#39;Centros&amp;#39;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="n"&gt;ax&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;set_title&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s2"&gt;&amp;quot;Agrupamiento s/modelo&amp;quot;&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;ax&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;legend&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;

&lt;span class="n"&gt;plt&lt;/span&gt;&lt;span class="o"&gt;.&lt;/span&gt;&lt;span class="n"&gt;show&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;    
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
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&lt;p&gt;Con esto doy por concluída esta introducción al &lt;a href="https://es.wikipedia.org/wiki/Aprendizaje_autom%C3%A1tico"&gt;Machine Learning&lt;/a&gt; con &lt;a href="http://python.org/"&gt;Python&lt;/a&gt;, espero les sea útil.&lt;/p&gt;
&lt;p&gt;Saludos!&lt;/p&gt;
&lt;p&gt;&lt;em&gt;Este post fue escrito utilizando IPython notebook. Pueden descargar este &lt;a href="https://github.com/relopezbriega/relopezbriega.github.io/blob/master/downloads/MachineLearningPython.ipynb"&gt;notebook&lt;/a&gt; o ver su version estática en &lt;a href="http://nbviewer.ipython.org/github/relopezbriega/relopezbriega.github.io/blob/master/downloads/MachineLearningPython.ipynb"&gt;nbviewer&lt;/a&gt;.&lt;/em&gt;&lt;/p&gt;

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&lt;/p&gt;</summary><category term="python"></category><category term="estadistica"></category><category term="programacion"></category><category term="Machine Learning"></category><category term="analisis de datos"></category></entry></feed>