class sklearn.feature_selection.GenericUnivariateSelect(score_func=<function f_classif>, mode=’percentile’, param=1e-05)
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Univariate feature selector with configurable strategy.
Read more in the User Guide.
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See also
f_classif
mutual_info_classif
chi2
f_regression
mutual_info_regression
SelectPercentile
SelectKBest
SelectFpr
SelectFdr
SelectFwe
>>> from sklearn.datasets import load_breast_cancer >>> from sklearn.feature_selection import GenericUnivariateSelect, chi2 >>> X, y = load_breast_cancer(return_X_y=True) >>> X.shape (569, 30) >>> transformer = GenericUnivariateSelect(chi2, 'k_best', param=20) >>> X_new = transformer.fit_transform(X, y) >>> X_new.shape (569, 20)
fit (X, y) | Run score function on (X, y) and get the appropriate features. |
fit_transform (X[, y]) | Fit to data, then transform it. |
get_params ([deep]) | Get parameters for this estimator. |
get_support ([indices]) | Get a mask, or integer index, of the features selected |
inverse_transform (X) | Reverse the transformation operation |
set_params (**params) | Set the parameters of this estimator. |
transform (X) | Reduce X to the selected features. |
__init__(score_func=<function f_classif>, mode=’percentile’, param=1e-05)
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fit(X, y)
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Run score function on (X, y) and get the appropriate features.
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fit_transform(X, y=None, **fit_params)
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Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
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get_params(deep=True)
[source]
Get parameters for this estimator.
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get_support(indices=False)
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Get a mask, or integer index, of the features selected
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inverse_transform(X)
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Reverse the transformation operation
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set_params(**params)
[source]
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.
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transform(X)
[source]
Reduce X to the selected features.
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© 2007–2018 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.GenericUnivariateSelect.html