sklearn.feature_selection
.SelectFwe¶

class
sklearn.feature_selection.
SelectFwe
(score_func=<function f_classif>, *, alpha=0.05)[source]¶ Filter: Select the pvalues corresponding to Familywise error rate
Read more in the User Guide.
 Parameters
 score_funccallable
Function taking two arrays X and y, and returning a pair of arrays (scores, pvalues). Default is f_classif (see below “See also”). The default function only works with classification tasks.
 alphafloat, optional
The highest uncorrected pvalue for features to keep.
 Attributes
 scores_arraylike of shape (n_features,)
Scores of features.
 pvalues_arraylike of shape (n_features,)
pvalues of feature scores.
See also
f_classif
ANOVA Fvalue between label/feature for classification tasks.
chi2
Chisquared stats of nonnegative features for classification tasks.
f_regression
Fvalue between label/feature for regression tasks.
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.
GenericUnivariateSelect
Univariate feature selector with configurable mode.
Examples
>>> from sklearn.datasets import load_breast_cancer >>> from sklearn.feature_selection import SelectFwe, chi2 >>> X, y = load_breast_cancer(return_X_y=True) >>> X.shape (569, 30) >>> X_new = SelectFwe(chi2, alpha=0.01).fit_transform(X, y) >>> X_new.shape (569, 15)
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.

__init__
(score_func=<function f_classif>, *, alpha=0.05)[source]¶ Initialize self. See help(type(self)) for accurate signature.

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 and y with optional parameters fit_params and returns a transformed version of X.
 Parameters
 X{arraylike, sparse matrix, dataframe} of shape (n_samples, n_features)
 yndarray of shape (n_samples,), default=None
Target values.
 **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
 paramsmapping of string to any
Parameter names mapped to their values.

get_support
(indices=False)[source]¶ Get a mask, or integer index, of the features selected
 Parameters
 indicesboolean (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 pipelines). 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
 selfobject
Estimator instance.