57 lines
1.5 KiB
ReStructuredText
57 lines
1.5 KiB
ReStructuredText
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.. _feature_selection_doc:
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=================
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Feature selection
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=================
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Univariate feature selection
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============================
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Univariate feature selection works by selecting the best features based on
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univariate statistical tests. It can seen as a preprocessing step
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to an estimator. The `scikit.learn` exposes feature selection routines
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a objects that implement the `transform` method. The k-best features
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can be selected based on:
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.. autofunction:: scikits.learn.feature_selection.univariate_selection.SelectKBest
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or by setting a percentile of features to keep using
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.. autofunction:: scikits.learn.feature_selection.univariate_selection.SelectPercentile
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or using common statistical quantities:
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.. autofunction:: scikits.learn.feature_selection.univariate_selection.SelectFpr
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.. autofunction:: scikits.learn.feature_selection.univariate_selection.SelectFdr
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.. autofunction:: scikits.learn.feature_selection.univariate_selection.SelectFwe
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These objects take as input a scoring function that returns
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univariate p-values.
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.. topic:: Examples:
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:ref:`example_plot_feature_selection.py`
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Feature scoring functions
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-------------------------
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.. warning::
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Beware not to use a regression scoring function with a classification problem.
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For classification
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..................
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.. autofunction:: scikits.learn.feature_selection.univariate_selection.f_classif
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For regression
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..............
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.. autofunction:: scikits.learn.feature_selection.univariate_selection.f_regression
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