SelectFpr#

classsklearn.feature_selection.SelectFpr(score_func=<functionf_classif>,*,alpha=0.05)[source]#

Filter: Select the pvalues below alpha based on a FPR test.

FPR test stands for False Positive Rate test. It controls the totalamount of false detections.

Read more in theUser 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 onlyworks with classification tasks.

alphafloat, default=5e-2

Features with p-values less thanalpha are selected.

Attributes:

scores_array-like of shape (n_features,)

Scores of features.

pvalues_array-like of shape (n_features,)

p-values of feature scores.

n_features_in_int

Number of features seen duringfit.

Added in version 0.24.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen duringfit. Defined only whenXhas feature names that are all strings.

Added in version 1.0.

See also

f_classif

ANOVA F-value between label/feature for classification tasks.

chi2

Chi-squared stats of non-negative features for classification tasks.

mutual_info_classif

Mutual information for a discrete target.

f_regression

F-value between label/feature for regression tasks.

mutual_info_regression

Mutual information for a continuous target.

SelectPercentile

Select features based on percentile of the highest scores.

SelectKBest

Select features based on the k highest scores.

SelectFdr

Select features based on an estimated false discovery rate.

SelectFwe

Select features based on family-wise error rate.

GenericUnivariateSelect

Univariate feature selector with configurable mode.

Examples

>>>fromsklearn.datasetsimportload_breast_cancer>>>fromsklearn.feature_selectionimportSelectFpr,chi2>>>X,y=load_breast_cancer(return_X_y=True)>>>X.shape(569, 30)>>>X_new=SelectFpr(chi2,alpha=0.01).fit_transform(X,y)>>>X_new.shape(569, 16)

fit(X,y=None)[source]#

Run score function on (X, y) and get the appropriate features.

Parameters:

Xarray-like of shape (n_samples, n_features)

The training input samples.

yarray-like of shape (n_samples,) or None

The target values (class labels in classification, real numbers inregression). If the selector is unsupervised theny can be set toNone.

Returns:

selfobject

Returns the instance itself.

fit_transform(X,y=None,**fit_params)[source]#

Fit to data, then transform it.

Fits transformer toX andy with optional parametersfit_paramsand returns a transformed version ofX.

Parameters:

Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), 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_feature_names_out(input_features=None)[source]#

Mask feature names according to selected features.

Parameters:

input_featuresarray-like of str or None, default=None

Input features.

• Ifinput_features isNone, thenfeature_names_in_ isused as feature names in. Iffeature_names_in_ is not defined,then the following input feature names are generated:["x0","x1",...,"x(n_features_in_-1)"].

• Ifinput_features is an array-like, theninput_features mustmatchfeature_names_in_ iffeature_names_in_ is defined.

Returns:

feature_names_outndarray of str objects

Transformed feature names.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please checkUser Guide on how the routingmechanism works.

Returns:

routingMetadataRequest

AMetadataRequest encapsulatingrouting information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator andcontained subobjects that are estimators.

Returns:

paramsdict

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, ratherthan a boolean mask.

Returns:

supportarray

An index that selects the retained features from a feature vector.Ifindices is False, this is a boolean array of shape[# input features], in which an element is True iff itscorresponding feature is selected for retention. Ifindices isTrue, this is an integer array of shape [# output features] whosevalues 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_originalarray of shape [n_samples, n_original_features]

X with columns of zeros inserted where features would havebeen removed bytransform.

set_output(*,transform=None)[source]#

Set output container.

SeeIntroducing the set_output APIfor an example on how to use the API.

Parameters:

transform{“default”, “pandas”, “polars”}, default=None

Configure output oftransform andfit_transform.

• "default": Default output format of a transformer

• "pandas": DataFrame output

• "polars": Polars output

• None: Transform configuration is unchanged

Added in version 1.4:"polars" option was added.

Returns:

selfestimator instance

Estimator instance.

set_params(**params)[source]#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects(such asPipeline). The latter haveparameters of the form<component>__<parameter> so that it’spossible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

transform(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.