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sklearn.feature_selection.SelectPercentile

class sklearn.feature_selection.SelectPercentile(score_func=<function f_classif at 0x5b44e60>, 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) 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 at 0x5b44e60>, percentile=10)
fit(X, y)

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)

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 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)

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)

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)

Set the parameters of this 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)

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.

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