sklearn.feature_selection.SelectFdr

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

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.

alphafloat, 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

Examples

>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.feature_selection import SelectFdr, chi2
>>> X, y = load_breast_cancer(return_X_y=True)
>>> X.shape
(569, 30)
>>> X_new = SelectFdr(chi2, alpha=0.01).fit_transform(X, y)
>>> X_new.shape
(569, 16)

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 0x7fbee7e6e200>, alpha=0.05)[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
Xarray-like, shape = [n_samples, n_features]

The training input samples.

yarray-like, 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. If indices 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 by transform.

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
transform(self, X)[source]

Reduce X to the selected features.

Parameters
Xarray of shape [n_samples, n_features]

The input samples.

Returns
X_rarray of shape [n_samples, n_selected_features]

The input samples with only the selected features.