Pushing the docs to dev/ for branch: master, commit 9acc606e6000b8ca8bf6644783fec2ef1efe2e4c

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import load_digits\nfrom sklearn.svm import SVC\nfrom sklearn.model_selection import validation_curve\n\ndigits = load_digits()\nX, y = digits.data, digits.target\n\nparam_range = np.logspace(-6, -1, 5)\ntrain_scores, test_scores = validation_curve(\n    SVC(), X, y, param_name=\"gamma\", param_range=param_range,\n    cv=10, scoring=\"accuracy\", n_jobs=1)\ntrain_scores_mean = np.mean(train_scores, axis=1)\ntrain_scores_std = np.std(train_scores, axis=1)\ntest_scores_mean = np.mean(test_scores, axis=1)\ntest_scores_std = np.std(test_scores, axis=1)\n\nplt.title(\"Validation Curve with SVM\")\nplt.xlabel(r\"$\\gamma$\")\nplt.ylabel(\"Score\")\nplt.ylim(0.0, 1.1)\nlw = 2\nplt.semilogx(param_range, train_scores_mean, label=\"Training score\",\n             color=\"darkorange\", lw=lw)\nplt.fill_between(param_range, train_scores_mean - train_scores_std,\n                 train_scores_mean + train_scores_std, alpha=0.2,\n                 color=\"darkorange\", lw=lw)\nplt.semilogx(param_range, test_scores_mean, label=\"Cross-validation score\",\n             color=\"navy\", lw=lw)\nplt.fill_between(param_range, test_scores_mean - test_scores_std,\n                 test_scores_mean + test_scores_std, alpha=0.2,\n                 color=\"navy\", lw=lw)\nplt.legend(loc=\"best\")\nplt.show()"
+        "print(__doc__)\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import load_digits\nfrom sklearn.svm import SVC\nfrom sklearn.model_selection import validation_curve\n\ndigits = load_digits()\nX, y = digits.data, digits.target\n\nparam_range = np.logspace(-6, -1, 5)\ntrain_scores, test_scores = validation_curve(\n    SVC(), X, y, param_name=\"gamma\", param_range=param_range,\n    cv=5, scoring=\"accuracy\", n_jobs=1)\ntrain_scores_mean = np.mean(train_scores, axis=1)\ntrain_scores_std = np.std(train_scores, axis=1)\ntest_scores_mean = np.mean(test_scores, axis=1)\ntest_scores_std = np.std(test_scores, axis=1)\n\nplt.title(\"Validation Curve with SVM\")\nplt.xlabel(r\"$\\gamma$\")\nplt.ylabel(\"Score\")\nplt.ylim(0.0, 1.1)\nlw = 2\nplt.semilogx(param_range, train_scores_mean, label=\"Training score\",\n             color=\"darkorange\", lw=lw)\nplt.fill_between(param_range, train_scores_mean - train_scores_std,\n                 train_scores_mean + train_scores_std, alpha=0.2,\n                 color=\"darkorange\", lw=lw)\nplt.semilogx(param_range, test_scores_mean, label=\"Cross-validation score\",\n             color=\"navy\", lw=lw)\nplt.fill_between(param_range, test_scores_mean - test_scores_std,\n                 test_scores_mean + test_scores_std, alpha=0.2,\n                 color=\"navy\", lw=lw)\nplt.legend(loc=\"best\")\nplt.show()"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -26,7 +26,7 @@
 param_range = np.logspace(-6, -1, 5)
 train_scores, test_scores = validation_curve(
     SVC(), X, y, param_name="gamma", param_range=param_range,
-    cv=10, scoring="accuracy", n_jobs=1)
+    cv=5, scoring="accuracy", n_jobs=1)
 train_scores_mean = np.mean(train_scores, axis=1)
 train_scores_std = np.std(train_scores, axis=1)
 test_scores_mean = np.mean(test_scores, axis=1)