sklearn.feature_selection
.SelectFdr¶

class
sklearn.feature_selection.
SelectFdr
(score_func=<function f_classif>, alpha=0.05)[source]¶ Filter: Select the pvalues for an estimated false discovery rate
This uses the BenjaminiHochberg 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 pvalue for features to keep.
Attributes:  scores_ : arraylike, shape=(n_features,)
Scores of features.
 pvalues_ : arraylike, shape=(n_features,)
pvalues of feature 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 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 familywise 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
(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. 
fit
(X, y)[source]¶ Run score function on (X, y) and get the appropriate features.
Parameters:  X : arraylike, shape = [n_samples, n_features]
The training input samples.
 y : arraylike, shape = [n_samples]
The target values (class labels in classification, real numbers in regression).
Returns:  self : object

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. Ifindices
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:

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