8.9.1. sklearn.feature_selection.SelectPercentile

class sklearn.feature_selection.SelectPercentile(score_func=<function f_classif at 0x38ea9b0>, percentile=10)

Select features according to a percentile of the highest scores.

Parameters:

score_func : callable

Function taking two arrays X and y, and returning a pair of arrays (scores, pvalues).

percentile : int, optional, default=10

Percent of features to keep.

Notes

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

Attributes

scores_	array-like, shape=(n_features,)	Scores of features.
pvalues_	array-like, shape=(n_features,)	p-values of feature scores.

Methods

fit(X, y)	Evaluate the score function on samples X with outputs y.
fit_transform(X[, y])	Fit to data, then transform it
get_params([deep])	Get parameters for the estimator
get_support([indices])	Return a mask, or list, of the features/indices selected.
inverse_transform(X)	Transform a new matrix using the selected features
set_params(**params)	Set the parameters of the estimator.
transform(X)	Transform a new matrix using the selected features

__init__(score_func=<function f_classif at 0x38ea9b0>, percentile=10)

fit(X, y)

Evaluate the score function on samples X with outputs y.

Records and selects features according to their scores.

fit_transform(X, y=None, **fit_params)

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)

Get parameters for the estimator

Parameters:

deep: boolean, optional :

If True, will return the parameters for this estimator and contained subobjects that are estimators.

get_support(indices=False)

Return a mask, or list, of the features/indices selected.

inverse_transform(X)

Transform a new matrix using the selected features

set_params(**params)

Set the parameters of the estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self :

transform(X)

Transform a new matrix using the selected features