Pushing the docs to dev/ for branch: master, commit 75a0985238494c8ee9c52b8c7b3f9bc308dfecf4

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "from __future__ import print_function\n\nfrom sklearn import datasets\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.metrics import classification_report\nfrom sklearn.svm import SVC\n\nprint(__doc__)\n\n# Loading the Digits dataset\ndigits = datasets.load_digits()\n\n# To apply an classifier on this data, we need to flatten the image, to\n# turn the data in a (samples, feature) matrix:\nn_samples = len(digits.images)\nX = digits.images.reshape((n_samples, -1))\ny = digits.target\n\n# Split the dataset in two equal parts\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, test_size=0.5, random_state=0)\n\n# Set the parameters by cross-validation\ntuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],\n                     'C': [1, 10, 100, 1000]},\n                    {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]\n\nscores = ['precision', 'recall']\n\nfor score in scores:\n    print(\"# Tuning hyper-parameters for %s\" % score)\n    print()\n\n    clf = GridSearchCV(SVC(C=1), tuned_parameters, cv=5,\n                       scoring='%s_macro' % score)\n    clf.fit(X_train, y_train)\n\n    print(\"Best parameters set found on development set:\")\n    print()\n    print(clf.best_params_)\n    print()\n    print(\"Grid scores on development set:\")\n    print()\n    means = clf.cv_results_['mean_test_score']\n    stds = clf.cv_results_['std_test_score']\n    for mean, std, params in zip(means, stds, clf.cv_results_['params']):\n        print(\"%0.3f (+/-%0.03f) for %r\"\n              % (mean, std * 2, params))\n    print()\n\n    print(\"Detailed classification report:\")\n    print()\n    print(\"The model is trained on the full development set.\")\n    print(\"The scores are computed on the full evaluation set.\")\n    print()\n    y_true, y_pred = y_test, clf.predict(X_test)\n    print(classification_report(y_true, y_pred))\n    print()\n\n# Note the problem is too easy: the hyperparameter plateau is too flat and the\n# output model is the same for precision and recall with ties in quality."
+        "from __future__ import print_function\n\nfrom sklearn import datasets\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.metrics import classification_report\nfrom sklearn.svm import SVC\n\nprint(__doc__)\n\n# Loading the Digits dataset\ndigits = datasets.load_digits()\n\n# To apply an classifier on this data, we need to flatten the image, to\n# turn the data in a (samples, feature) matrix:\nn_samples = len(digits.images)\nX = digits.images.reshape((n_samples, -1))\ny = digits.target\n\n# Split the dataset in two equal parts\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, test_size=0.5, random_state=0)\n\n# Set the parameters by cross-validation\ntuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],\n                     'C': [1, 10, 100, 1000]},\n                    {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]\n\nscores = ['precision', 'recall']\n\nfor score in scores:\n    print(\"# Tuning hyper-parameters for %s\" % score)\n    print()\n\n    clf = GridSearchCV(SVC(), tuned_parameters, cv=5,\n                       scoring='%s_macro' % score)\n    clf.fit(X_train, y_train)\n\n    print(\"Best parameters set found on development set:\")\n    print()\n    print(clf.best_params_)\n    print()\n    print(\"Grid scores on development set:\")\n    print()\n    means = clf.cv_results_['mean_test_score']\n    stds = clf.cv_results_['std_test_score']\n    for mean, std, params in zip(means, stds, clf.cv_results_['params']):\n        print(\"%0.3f (+/-%0.03f) for %r\"\n              % (mean, std * 2, params))\n    print()\n\n    print(\"Detailed classification report:\")\n    print()\n    print(\"The model is trained on the full development set.\")\n    print(\"The scores are computed on the full evaluation set.\")\n    print()\n    y_true, y_pred = y_test, clf.predict(X_test)\n    print(classification_report(y_true, y_pred))\n    print()\n\n# Note the problem is too easy: the hyperparameter plateau is too flat and the\n# output model is the same for precision and recall with ties in quality."
       ], 
       "outputs": [], 
       "metadata": {

dev/_downloads/auto_examples_python.zip

@@ -50,7 +50,7 @@
     print("# Tuning hyper-parameters for %s" % score)
     print()
 
-    clf = GridSearchCV(SVC(C=1), tuned_parameters, cv=5,
+    clf = GridSearchCV(SVC(), tuned_parameters, cv=5,
                        scoring='%s_macro' % score)
     clf.fit(X_train, y_train)