sklearn.feature_selection.SelectFwe

class sklearn.feature_selection.SelectFwe(score_func=<function f_classif>, alpha=0.05)

Filter: Select the p-values corresponding to Family-wise error rate

Read more in the User Guide.

Parameters:

score_func : callable

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.

alpha : float, optional

The highest uncorrected p-value for features to keep.

Attributes:

scores_ : array-like, shape=(n_features,)

Scores of features.

pvalues_ : array-like, shape=(n_features,)

p-values of feature scores.

See also

f_classif

ANOVA F-value between label/feature for classification tasks.

chi2

Chi-squared stats of non-negative features for classification tasks.

f_regression

F-value 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
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>, alpha=0.05)

Initialize self. See help(type(self)) for accurate signature.

fit(X, y)

Run score function on (X, y) and get the appropriate features.

Parameters:

X : array-like, shape = [n_samples, n_features]

The training input samples.

y : array-like, shape = [n_samples]

The target values (class labels in classification, real numbers in regression).

Returns:

self : object

fit_transform(X, y=None, **fit_params)

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 : numpy array of shape [n_samples, n_features]

Training set.

y : numpy array of shape [n_samples]

Target values.

Returns:

X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep : boolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params : mapping of string to any

Parameter names mapped to their values.

get_support(indices=False)

Get a mask, or integer index, of the features selected

Parameters:

indices : boolean (default False)

If True, the return value will be an array of integers, rather than a boolean mask.

Returns:

support : array

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. If indices is True, this is an integer array of shape [# output features] whose values are indices into the input feature vector.

inverse_transform(X)

Reverse the transformation operation

Parameters:

X : array of shape [n_samples, n_selected_features]

The input samples.

Returns:

X_r : array of shape [n_samples, n_original_features]

X with columns of zeros inserted where features would have been removed by transform.

set_params(**params)

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.

Returns:

self

transform(X)

Reduce X to the selected features.

Parameters:

X : array of shape [n_samples, n_features]

The input samples.

Returns:

X_r : array of shape [n_samples, n_selected_features]

The input samples with only the selected features.