Pushing the docs to dev/ for branch: master, commit 6e98eec04319ba5e4e250b9af33ade31a18e4a91

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -15,7 +15,7 @@
     }, 
     {
       "source": [
-        "\n# Nested versus non-nested cross-validation\n\n\nThis example compares non-nested and nested cross-validation strategies on a\nclassifier of the iris data set. Nested cross-validation (CV) is often used to\ntrain a model in which hyperparameters also need to be optimized. Nested CV\nestimates the generalization error of the underlying model and its\n(hyper)parameter search. Choosing the parameters that maximize non-nested CV\nbiases the model to the dataset, yielding an overly-optimistic score.\n\nModel selection without nested CV uses the same data to tune model parameters\nand evaluate model performance. Information may thus \"leak\" into the model\nand overfit the data. The magnitude of this effect is primarily dependent on\nthe size of the dataset and the stability of the model. See Cawley and Talbot\n[1]_ for an analysis of these issues.\n\nTo avoid this problem, nested CV effectively uses a series of\ntrain/validation/test set splits. In the inner loop, the score is approximately\nmaximized by fitting a model to each training set, and then directly maximized\nin selecting (hyper)parameters over the validation set. In the outer loop,\ngeneralization error is estimated by averaging test set scores over several\ndataset splits.\n\nThe example below uses a support vector classifier with a non-linear kernel to\nbuild a model with optimized hyperparameters by grid search. We compare the\nperformance of non-nested and nested CV strategies by taking the difference\nbetween their scores.\n\n.. topic:: See Also:\n\n    - `cross_validation`\n    - `grid_search`\n\n.. topic:: References:\n\n    .. [1] `Cawley, G.C.; Talbot, N.L.C. On over-fitting in model selection and\n     subsequent selection bias in performance evaluation.\n     J. Mach. Learn. Res 2010,11, 2079-2107.\n     <http://jmlr.csail.mit.edu/papers/volume11/cawley10a/cawley10a.pdf>`_\n\n\n"
+        "\n# Nested versus non-nested cross-validation\n\n\nThis example compares non-nested and nested cross-validation strategies on a\nclassifier of the iris data set. Nested cross-validation (CV) is often used to\ntrain a model in which hyperparameters also need to be optimized. Nested CV\nestimates the generalization error of the underlying model and its\n(hyper)parameter search. Choosing the parameters that maximize non-nested CV\nbiases the model to the dataset, yielding an overly-optimistic score.\n\nModel selection without nested CV uses the same data to tune model parameters\nand evaluate model performance. Information may thus \"leak\" into the model\nand overfit the data. The magnitude of this effect is primarily dependent on\nthe size of the dataset and the stability of the model. See Cawley and Talbot\n[1]_ for an analysis of these issues.\n\nTo avoid this problem, nested CV effectively uses a series of\ntrain/validation/test set splits. In the inner loop (here executed by\n:class:`GridSearchCV <sklearn.model_selection.GridSearchCV>`), the score is\napproximately maximized by fitting a model to each training set, and then\ndirectly maximized in selecting (hyper)parameters over the validation set. In\nthe outer loop (here in :func:`cross_val_score\n<sklearn.model_selection.cross_val_score>`), generalization error is estimated\nby averaging test set scores over several dataset splits.\n\nThe example below uses a support vector classifier with a non-linear kernel to\nbuild a model with optimized hyperparameters by grid search. We compare the\nperformance of non-nested and nested CV strategies by taking the difference\nbetween their scores.\n\n.. topic:: See Also:\n\n    - `cross_validation`\n    - `grid_search`\n\n.. topic:: References:\n\n    .. [1] `Cawley, G.C.; Talbot, N.L.C. On over-fitting in model selection and\n     subsequent selection bias in performance evaluation.\n     J. Mach. Learn. Res 2010,11, 2079-2107.\n     <http://jmlr.csail.mit.edu/papers/volume11/cawley10a/cawley10a.pdf>`_\n\n\n"
       ], 
       "cell_type": "markdown", 
       "metadata": {}

dev/_downloads/auto_examples_python.zip

@@ -17,11 +17,13 @@
 [1]_ for an analysis of these issues.
 
 To avoid this problem, nested CV effectively uses a series of
-train/validation/test set splits. In the inner loop, the score is approximately
-maximized by fitting a model to each training set, and then directly maximized
-in selecting (hyper)parameters over the validation set. In the outer loop,
-generalization error is estimated by averaging test set scores over several
-dataset splits.
+train/validation/test set splits. In the inner loop (here executed by
+:class:`GridSearchCV <sklearn.model_selection.GridSearchCV>`), the score is
+approximately maximized by fitting a model to each training set, and then
+directly maximized in selecting (hyper)parameters over the validation set. In
+the outer loop (here in :func:`cross_val_score
+<sklearn.model_selection.cross_val_score>`), generalization error is estimated
+by averaging test set scores over several dataset splits.
 
 The example below uses a support vector classifier with a non-linear kernel to
 build a model with optimized hyperparameters by grid search. We compare the