overfitting table

slides/regularization/rsrc/table_overfitting.R

@@ -0,0 +1,52 @@
+# ------------------------------------------------------------------------------
+# intro
+# TABLE: 
+#   train and test MSE table using Neural Network and CART (overfitting).
+# DATA: mtcars
+# ------------------------------------------------------------------------------
+
+library(nnet)
+library(xtable)
+library(mlr3)
+library(mlr3learners)
+set.seed(123)
+
+# DATA -------------------------------------------------------------------------
+
+lgr::get_logger("mlr3")$set_threshold("info")
+
+task = tsk("mtcars")
+
+lrn1 = lrn("regr.nnet", size = 100, maxit = 20000, MaxNWts = 10000, decay = 0, abstol = 1e-7)
+lrn1$encapsulate = c(train = "evaluate", predict = "evaluate")
+lrn2 = lrn("regr.rpart", minsplit = 2, cp = 0)
+
+my_learners = list(lrn1, lrn2)
+for (x in my_learners){
+  x$predict_sets = c("train", "test")
+}
+
+bg = benchmark_grid(task, my_learners, rsmp("cv", folds = 10))
+
+bmr = benchmark(bg)
+
+m1 = msr("regr.mse", predict_sets = c("test"), id = "mse-test")
+m2 = msr("regr.mse", predict_sets = c("train"), id = "mse-train")
+
+a = bmr$aggregate(measures = list(m1, m2))
+print(a)
+
+# TABLE ------------------------------------------------------------------------
+
+# Create a 2x2 comparison table with rounded results
+res = as.data.frame(a)
+res = res[, c("mse-train", "mse-test")]
+
+rownames(res) = c("Neural Network", "CART")
+colnames(res) = c("Train MSE", "Test MSE")
+
+latex_tab = xtable(res)
+
+print(latex_tab, file = "table_overfitting.tex", include.rownames = TRUE, include.colnames = TRUE, comment = FALSE)
+
+

slides/regularization/rsrc/table_overfitting.tex

@@ -0,0 +1,11 @@
+\begin{table}[ht]
+\centering
+\begin{tabular}{rrr}
+  \hline
+ & Train MSE & Test MSE \\ 
+  \hline
+Neural Network & 1.47 & 345.84 \\ 
+  CART & 0.00 & 6.91 \\ 
+   \hline
+\end{tabular}
+\end{table}

slides/regularization/slides-regu-intro.tex

@@ -92,17 +92,7 @@
 
 \lz \lz
 
-\begin{table}[ht]
-\centering
-\begin{tabular}{rrr}
-  \hline
- & Train MSE & Test MSE \\ 
-  \hline
-Neural Network & 3.68 & 19.98 \\ 
-  CART & 0.00 & 10.21 \\ 
-   \hline
-\end{tabular}
-\end{table}
+\input{rsrc/table_overfitting.tex}
 
 \lz \lz