sklearn.feature_selection.SelectKBest

class sklearn.feature_selection.SelectKBest(score_func=<function f_classif>, *, k=10)[source]

Select features according to the k highest scores.

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) or a single array with scores. Default is f_classif (see below “See Also”). The default function only works with classification tasks.

New in version 0.18.

kint or “all”, default=10

Number of top features to select. The “all” option bypasses selection, for use in a parameter search.

Attributes:
scores_array-like of shape (n_features,)

Scores of features.

pvalues_array-like of shape (n_features,)

p-values of feature scores, None if score_func returned only scores.

n_features_in_int

Number of features seen during fit.

New in version 0.24.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when X has feature names that are all strings.

New in version 1.0.

See also

f_classif

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

mutual_info_classif

Mutual information for a discrete target.

chi2

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

f_regression

F-value 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.

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 family-wise error rate.

GenericUnivariateSelect

Univariate feature selector with configurable mode.

Notes

Ties between features with equal scores will be broken in an unspecified way.

Examples

>>> from sklearn.datasets import load_digits
>>> from sklearn.feature_selection import SelectKBest, chi2
>>> X, y = load_digits(return_X_y=True)
>>> X.shape
(1797, 64)
>>> X_new = SelectKBest(chi2, k=20).fit_transform(X, y)
>>> X_new.shape
(1797, 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_feature_names_out([input_features])

Mask feature names according to selected features.

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.

fit(X, y)[source]

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

Parameters:
Xarray-like of shape (n_samples, n_features)

The training input samples.

yarray-like of shape (n_samples,)

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

Returns:
selfobject

Returns the instance itself.

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:
Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like 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_feature_names_out(input_features=None)[source]

Mask feature names according to selected features.

Parameters:
input_featuresarray-like of str or None, default=None

Input features.

  • If input_features is None, then feature_names_in_ is used as feature names in. If feature_names_in_ is not defined, then the following input feature names are generated: ["x0", "x1", ..., "x(n_features_in_ - 1)"].

  • If input_features is an array-like, then input_features must match feature_names_in_ if feature_names_in_ is defined.

Returns:
feature_names_outndarray of str objects

Transformed feature names.

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. 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)[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 by transform.

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.

transform(X)[source]

Reduce X to the selected features.

Parameters:
Xarray of shape [n_samples, n_features]

The input samples.

Returns:
X_rarray of shape [n_samples, n_selected_features]

The input samples with only the selected features.

Examples using sklearn.feature_selection.SelectKBest

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Release Highlights for scikit-learn 1.1

Release Highlights for scikit-learn 1.1
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