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_funccallable, default=f_classif
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, default=5e2
The highest uncorrected pvalue for features to keep.
 Attributes
 scores_arraylike of shape (n_features,)
Scores of features.
 pvalues_arraylike of 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
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
 Xarraylike of shape (n_samples, n_features)
The training input samples.
 yarraylike of shape (n_samples,)
The target values (class labels in classification, real numbers in regression).
 Returns
 selfobject

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{arraylike, sparse matrix, dataframe} of shape (n_samples, n_features)
Input samples.
 yndarray of shape (n_samples,), default=None
Target values (None for unsupervised transformations).
 **fit_paramsdict
Additional fit parameters.
 Returns
 X_newndarray array of shape (n_samples, n_features_new)
Transformed array.

get_params
(deep=True)[source]¶ Get parameters for this estimator.
 Parameters
 deepbool, default=True
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
(indices=False)[source]¶ Get a mask, or integer index, of the features selected
 Parameters
 indicesbool, 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. 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
 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 bytransform
.

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. Parameters
 **paramsdict
Estimator parameters.
 Returns
 selfobject
Estimator instance.