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_funccallable
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.
 percentileint, optional, default=10
Percent of features to keep.
 Attributes
 scores_arraylike, shape=(n_features,)
Scores of features.
 pvalues_arraylike, shape=(n_features,)
pvalues of feature scores, None if
score_func
returned only scores.
See also
f_classif
ANOVA Fvalue between label/feature for classification tasks.
mutual_info_classif
Mutual information for a discrete target.
chi2
Chisquared stats of nonnegative features for classification tasks.
f_regression
Fvalue 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 familywise 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
(self, X, y)Run score function on (X, y) and get the appropriate features.
fit_transform
(self, X[, y])Fit to data, then transform it.
get_params
(self[, deep])Get parameters for this estimator.
get_support
(self[, indices])Get a mask, or integer index, of the features selected
inverse_transform
(self, X)Reverse the transformation operation
set_params
(self, \*\*params)Set the parameters of this estimator.
transform
(self, X)Reduce X to the selected features.

__init__
(self, score_func=<function f_classif at 0x7efe348c0320>, percentile=10)[source]¶ Initialize self. See help(type(self)) for accurate signature.

fit
(self, X, y)[source]¶ Run score function on (X, y) and get the appropriate features.
 Parameters
 Xarraylike, shape = [n_samples, n_features]
The training input samples.
 yarraylike, shape = [n_samples]
The target values (class labels in classification, real numbers in regression).
 Returns
 selfobject

fit_transform
(self, 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
 Xnumpy array of shape [n_samples, n_features]
Training set.
 ynumpy array of shape [n_samples]
Target values.
 Returns
 X_newnumpy array of shape [n_samples, n_features_new]
Transformed array.

get_params
(self, deep=True)[source]¶ Get parameters for this estimator.
 Parameters
 deepboolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
 Returns
 paramsmapping of string to any
Parameter names mapped to their values.

get_support
(self, indices=False)[source]¶ Get a mask, or integer index, of the features selected
 Parameters
 indicesboolean (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. Ifindices
is True, this is an integer array of shape [# output features] whose values are indices into the input feature vector.

inverse_transform
(self, 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 bytransform
.

set_params
(self, **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