sklearn.feature_selection.SelectPercentile

class sklearn.feature_selection.SelectPercentile(score_func=<function f_classif>, 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.

Examples

>>> from sklearn.datasets import load_digits
>>> from sklearn.feature_selection import SelectPercentile, chi2
>>> X, y = load_digits(return_X_y=True)
>>> X.shape
(1797, 64)
>>> X_new = SelectPercentile(chi2, percentile=10).fit_transform(X, y)
>>> X_new.shape
(1797, 7)

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>, 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
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.