sklearn.feature_selection.SelectFdr

class sklearn.feature_selection.SelectFdr(score_func=, alpha=0.05) [source]

Filter: Select the p-values for an estimated false discovery rate

This uses the Benjamini-Hochberg procedure. alpha is an upper bound on the expected false discovery rate.

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). Default is f_classif (see below “See also”). The default function only works with classification tasks. alpha : float, optional The highest uncorrected p-value for features to keep.
Attributes:	scores_ : array-like, shape=(n_features,) Scores of features. pvalues_ : array-like, shape=(n_features,) p-values of feature 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 contnuous target.

SelectPercentile

Select features based on percentile of the highest scores.

SelectKBest

Select features based on the k highest scores.

SelectFpr

Select features based on a false positive rate test.

SelectFwe

Select features based on family-wise error rate.

GenericUnivariateSelect

Univariate feature selector with configurable mode.

References

https://en.wikipedia.org/wiki/False_discovery_rate

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=, alpha=0.05) [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.