sklearn.feature_selection
.GenericUnivariateSelect¶

class
sklearn.feature_selection.
GenericUnivariateSelect
(score_func=<function f_classif>, *, mode='percentile', param=1e05)[source]¶ Univariate feature selector with configurable strategy.
Read more in the User Guide.
 Parameters
 score_funccallable, default=f_classif
Function taking two arrays X and y, and returning a pair of arrays (scores, pvalues). For modes ‘percentile’ or ‘kbest’ it can return a single array scores.
 mode{‘percentile’, ‘k_best’, ‘fpr’, ‘fdr’, ‘fwe’}, default=’percentile’
Feature selection mode.
 paramfloat or int depending on the feature selection mode, default=1e5
Parameter of the corresponding mode.
 Attributes
 scores_arraylike of shape (n_features,)
Scores of features.
 pvalues_arraylike of shape (n_features,)
pvalues of feature scores, None if
score_func
returned scores only.
See also
f_classif
ANOVA Fvalue between label/feature for classification tasks.
mutual_info_classif
Mutual information for a discrete target.
chi2
Chisquared stats of nonnegative features for classification tasks.
f_regression
Fvalue between label/feature for regression tasks.
mutual_info_regression
Mutual information for a continuous target.
SelectPercentile
Select features based on percentile of the highest scores.
SelectKBest
Select features based on the k highest scores.
SelectFpr
Select features based on a false positive rate test.
SelectFdr
Select features based on an estimated false discovery rate.
SelectFwe
Select features based on familywise error rate.
Examples
>>> 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, mode='k_best', param=20) >>> X_new = transformer.fit_transform(X, y) >>> X_new.shape (569, 20)
Methods
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
Reverse the transformation operation
set_params
(**params)Set the parameters of this estimator.
transform
(X)Reduce X to the selected features.

fit
(X, y)[source]¶ Run score function on (X, y) and get the appropriate features.
 Parameters
 Xarraylike of shape (n_samples, n_features)
The training input samples.
 yarraylike of shape (n_samples,)
The target values (class labels in classification, real numbers in regression).
 Returns
 selfobject

fit_transform
(X, y=None, **fit_params)[source]¶ Fit to data, then transform it.
Fits transformer to
X
andy
with optional parametersfit_params
and returns a transformed version ofX
. Parameters
 Xarraylike of shape (n_samples, n_features)
Input samples.
 yarraylike of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
 **fit_paramsdict
Additional fit parameters.
 Returns
 X_newndarray array of shape (n_samples, n_features_new)
Transformed array.

get_params
(deep=True)[source]¶ Get parameters for this estimator.
 Parameters
 deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
 Returns
 paramsdict
Parameter names mapped to their values.

get_support
(indices=False)[source]¶ Get a mask, or integer index, of the features selected
 Parameters
 indicesbool, default=False
If True, the return value will be an array of integers, rather than a boolean mask.
 Returns
 supportarray
An index that selects the retained features from a feature vector. If
indices
is False, this is a boolean array of shape [# input features], in which an element is True iff its corresponding feature is selected for retention. Ifindices
is True, this is an integer array of shape [# output features] whose values are indices into the input feature vector.

inverse_transform
(X)[source]¶ Reverse the transformation operation
 Parameters
 Xarray of shape [n_samples, n_selected_features]
The input samples.
 Returns
 X_rarray of shape [n_samples, n_original_features]
X
with columns of zeros inserted where features would have been removed bytransform
.

set_params
(**params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object. Parameters
 **paramsdict
Estimator parameters.
 Returns
 selfestimator instance
Estimator instance.