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DOC Modify documentation/examples for the new model_selection module
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‎doc/developers/contributing.rst‎

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@@ -794,7 +794,7 @@ adheres to the scikit-learn interface and standards by running
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The main motivation to make a class compatible to the scikit-learn estimator
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interface might be that you want to use it together with model assessment and
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selection tools such as :class:`grid_search.GridSearchCV`.
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selection tools such as :class:`model_selection.GridSearchCV`.
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For this to work, you need to implement the following interface.
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If a dependency on scikit-learn is okay for your code,
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Parameters and init
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-------------------
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As :class:`grid_search.GridSearchCV` uses ``set_params``
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As :class:`model_selection.GridSearchCV` uses ``set_params``
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to apply parameter setting to estimators,
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it is essential that calling ``set_params`` has the same effect
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as setting parameters using the ``__init__`` method.
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Cloning
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-------
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For using :class:`grid_search.GridSearch` or any functionality of the
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:mod:`cross_validation` module, an estimator must support the ``base.clone``
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function to replicate an estimator.
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For use with the :mod:`model_selection` module,
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an estimator must support the ``base.clone`` function to replicate an estimator.
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This can be done by providing a ``get_params`` method.
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If ``get_params`` is present, then ``clone(estimator)`` will be an instance of
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``type(estimator)`` on which ``set_params`` has been called with clones of
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Estimator types
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---------------
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Some common functionality depends on the kind of estimator passed.
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For example, cross-validation in :class:`grid_search.GridSearchCV` and
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:func:`cross_validation.cross_val_score` defaults to being stratified when used
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For example, cross-validation in :class:`model_selection.GridSearchCV` and
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:func:`model_selection.cross_val_score` defaults to being stratified when used
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on a classifier, but not otherwise. Similarly, scorers for average precision
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that take a continuous prediction need to call ``decision_function`` for classifiers,
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but ``predict`` for regressors. This distinction between classifiers and regressors

‎doc/model_selection.rst‎

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modules/grid_search
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modules/model_evaluation
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modules/model_persistence
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modules/learning_curve

‎doc/modules/classes.rst‎

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=============
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API Reference
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=============
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covariance.graph_lasso
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.. _cross_validation_ref:
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:mod:`sklearn.cross_validation`: Cross Validation
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=================================================
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:mod:`sklearn.model_selection`: Model Selection
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===============================================
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.. automodule:: sklearn.cross_validation
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.. automodule:: sklearn.model_selection
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:no-members:
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:no-inherited-members:
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**User guide:** See the :ref:`cross_validation` section for further details.
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**User guide:** See the :ref:`cross_validation`, :ref:`grid_search` and
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:ref:`learning_curve` sections for further details.
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Splitters
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---------
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.. currentmodule:: sklearn
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.. autosummary::
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:toctree: generated/
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:template: class.rst
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model_selection.KFold
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model_selection.LabelKFold
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model_selection.StratifiedKFold
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model_selection.LeaveOneLabelOut
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model_selection.LeavePLabelOut
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model_selection.LeaveOneOut
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model_selection.LeavePOut
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model_selection.ShuffleSplit
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model_selection.LabelShuffleSplit
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model_selection.StratifiedShuffleSplit
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model_selection.PredefinedSplit
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Splitter helper
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---------------
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.. currentmodule:: sklearn
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.. autosummary::
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:toctree: generated/
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:template: function.rst
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model_selection.train_test_split
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Parameter tuners
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----------------
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.. currentmodule:: sklearn
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.. autosummary::
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:toctree: generated/
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:template: class.rst
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cross_validation.KFold
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cross_validation.LabelKFold
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cross_validation.LabelShuffleSplit
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cross_validation.LeaveOneLabelOut
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cross_validation.LeaveOneOut
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cross_validation.LeavePLabelOut
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cross_validation.LeavePOut
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cross_validation.PredefinedSplit
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cross_validation.ShuffleSplit
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cross_validation.StratifiedKFold
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cross_validation.StratifiedShuffleSplit
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model_selection.GridSearchCV
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model_selection.RandomizedSearchCV
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Parameter search utilites
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-------------------------
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.. currentmodule:: sklearn
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.. autosummary::
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:toctree: generated/
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:template: class.rst
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model_selection.ParameterGrid
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model_selection.ParameterSampler
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Scorers
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-------
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.. currentmodule:: sklearn
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.. autosummary::
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:toctree: generated/
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:template: function.rst
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cross_validation.train_test_split
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cross_validation.cross_val_score
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cross_validation.cross_val_predict
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cross_validation.permutation_test_score
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cross_validation.check_cv
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model_selection.cross_val_score
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model_selection.cross_val_predict
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model_selection.permutation_test_score
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model_selection.check_cv
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Diagnostic utilities to evaluate the Bias-Variance trade-off
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------------------------------------------------------------
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.. currentmodule:: sklearn
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.. autosummary::
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:toctree: generated/
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:template: function.rst
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model_selection.learning_curve
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model_selection.validation_curve
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.. _datasets_ref:
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gaussian_process.kernels.CompoundKernel
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gaussian_process.kernels.Hyperparameter
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.. _grid_search_ref:
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:mod:`sklearn.grid_search`: Grid Search
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=======================================
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.. automodule:: sklearn.grid_search
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:no-members:
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:no-inherited-members:
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**User guide:** See the :ref:`grid_search` section for further details.
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.. currentmodule:: sklearn
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.. autosummary::
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:toctree: generated/
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:template: class.rst
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grid_search.GridSearchCV
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grid_search.ParameterGrid
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grid_search.ParameterSampler
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grid_search.RandomizedSearchCV
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.. _isotonic_ref:
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discriminant_analysis.QuadraticDiscriminantAnalysis
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.. _learning_curve_ref:
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:mod:`sklearn.learning_curve` Learning curve evaluation
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=======================================================
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.. automodule:: sklearn.learning_curve
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:no-members:
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:no-inherited-members:
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.. currentmodule:: sklearn
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.. autosummary::
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:toctree: generated/
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:template: function.rst
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learning_curve.learning_curve
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learning_curve.validation_curve
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.. _linear_model_ref:
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:mod:`sklearn.linear_model`: Generalized Linear Models

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