`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.

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.

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>, k=10)[source]¶

fit(X, y)[source]¶

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

Returns self.

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 : 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)[source]¶

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)[source]¶

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)[source]¶

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)[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.