underscore

HW3.ipynb

@@ -554,9 +554,7 @@
       "Using the `critics` dataframe, compute a pair of numerical X, Y arrays where:\n",
       "    \n",
       " * X is a `(nreview, nwords)` array. Each row corresponds to a bag-of-words representation for a single review. This will be the *input* to your model.\n",
-      " * Y is a `nreview`-element 1/0 array, encoding whether a review is Fresh (1) or Rotten (0). This is the desired *output* from your model.\n",
-      " \n",
-      "Make sure to remove items with no review text"
+      " * Y is a `nreview`-element 1/0 array, encoding whether a review is Fresh (1) or Rotten (0). This is the desired *output* from your model.\n"
      ]
     },
     {
@@ -765,7 +763,7 @@
       "\n",
       "As discussed in lecture and HW2, a common technique for choosing appropriate values for these parameters is **cross-validation**. Let's choose good parameters by maximizing the cross-validated log-likelihood.\n",
       "\n",
-      "**3.6** Using `clf.predict_logproba`, write a function that computes the log-likelihood of a dataset"
+      "**3.6** Using `clf.predict_log_proba`, write a function that computes the log-likelihood of a dataset"
      ]
     },
     {