add return_train_score

Author: amueller

01-introduction.ipynb

@@ -304,7 +304,7 @@
    ],
    "source": [
     "import pandas as pd\n",
-    "from IPython.display import display\n",
+    "from IPython import display\n",
     "\n",
     "# create a simple dataset of people\n",
     "data = {'Name': [\"John\", \"Anna\", \"Peter\", \"Linda\"],\n",

05-model-evaluation-and-improvement.ipynb

@@ -677,7 +677,8 @@
    "source": [
     "from sklearn.model_selection import GridSearchCV\n",
     "from sklearn.svm import SVC\n",
-    "grid_search = GridSearchCV(SVC(), param_grid, cv=5)"
+    "grid_search = GridSearchCV(SVC(), param_grid, cv=5,\n",
+    "                          return_train_score=True)"
    ]
   },
   {
@@ -1045,7 +1046,8 @@
     }
    ],
    "source": [
-    "grid_search = GridSearchCV(SVC(), param_grid, cv=5)\n",
+    "grid_search = GridSearchCV(SVC(), param_grid, cv=5,\n",
+    "                          return_train_score=True)\n",
     "grid_search.fit(X_train, y_train)\n",
     "print(\"Best parameters: {}\".format(grid_search.best_params_))\n",
     "print(\"Best cross-validation score: {:.2f}\".format(grid_search.best_score_))"

mglearn/plot_grid_search.py

@@ -14,7 +14,8 @@ def plot_cross_val_selection():
 
     param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100],
                   'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}
-    grid_search = GridSearchCV(SVC(), param_grid, cv=5)
+    grid_search = GridSearchCV(SVC(), param_grid, cv=5,
+                               return_train_score=True)
     grid_search.fit(X_trainval, y_trainval)
     results = pd.DataFrame(grid_search.cv_results_)[15:]