feature_selection.SelectPercentile()

class sklearn.feature_selection.SelectPercentile(score_func=, percentile=10) [source]

Select features according to a percentile of the highest scores.

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) or a single array with scores. Default is f_classif (see below ?See also?). The default function only works with classification tasks. percentile : int, optional, default=10 Percent of features to keep.
Attributes:	scores_ : array-like, shape=(n_features,) Scores of features. pvalues_ : array-like, shape=(n_features,) p-values of feature scores, None if score_func returned only 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.

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 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=, percentile=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.

Returns:	self :

transform(X) [source]

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.

Examples using sklearn.feature_selection.SelectPercentile

Feature agglomeration vs. univariate selection

Univariate Feature Selection

SVM-Anova: SVM with univariate feature selection