Pushing the docs to dev/ for branch: master, commit e55d0bcba5ed5a270d140fefdfa4b1ee2ed1a1eb

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV\n\nMultiple metric parameter search can be done by setting the ``scoring``\nparameter to a list of metric scorer names or a dict mapping the scorer names\nto the scorer callables.\n\nThe scores of all the scorers are available in the ``cv_results_`` dict at keys\nending in ``'_<scorer_name>'`` (``'mean_test_precision'``,\n``'rank_test_precision'``, etc...)\n\nThe ``best_estimator_``, ``best_index_``, ``best_score_`` and ``best_params_``\ncorrespond to the scorer (key) that is set to the ``refit`` attribute.\n\n"
+        "\n# Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV\n\n\nMultiple metric parameter search can be done by setting the ``scoring``\nparameter to a list of metric scorer names or a dict mapping the scorer names\nto the scorer callables.\n\nThe scores of all the scorers are available in the ``cv_results_`` dict at keys\nending in ``'_<scorer_name>'`` (``'mean_test_precision'``,\n``'rank_test_precision'``, etc...)\n\nThe ``best_estimator_``, ``best_index_``, ``best_score_`` and ``best_params_``\ncorrespond to the scorer (key) that is set to the ``refit`` attribute.\n\n"
       ]
     },
     {

dev/_downloads/auto_examples_python.zip

@@ -1,4 +1,7 @@
-"""Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV
+"""
+============================================================================
+Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV
+============================================================================
 
 Multiple metric parameter search can be done by setting the ``scoring``
 parameter to a list of metric scorer names or a dict mapping the scorer names