sklearn.feature_selection
.SelectFpr¶

class
sklearn.feature_selection.
SelectFpr
(score_func=<function f_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 total amount of false detections.
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 pvalue for features to be kept.
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.
chi2
 Chisquared stats of nonnegative features for classification tasks.
f_regression
 Fvalue between label/feature for regression tasks.
mutual_info_regression
 Mutual information between features and the 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 familywise error rate.
GenericUnivariateSelect
 Univariate feature selector with configurable mode.
Examples
>>> from sklearn.datasets import load_breast_cancer >>> from sklearn.feature_selection import SelectFpr, 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)
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. 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