sklearn.feature_selection.GenericUnivariateSelect  
class sklearn.feature_selection.GenericUnivariateSelect(score_func=<function f_classif>, *, mode='percentile', param=1e-05) [source]
 
Univariate feature selector with configurable strategy. 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). For modes ‘percentile’ or ‘kbest’ it can return a single array scores.  
mode{‘percentile’, ‘k_best’, ‘fpr’, ‘fdr’, ‘fwe’}, default=’percentile’ 

Feature selection mode.  
paramfloat or int depending on the feature selection mode, default=1e-5 

Parameter of the corresponding mode.    Attributes 
 
scores_array-like of shape (n_features,) 

Scores of features.  
pvalues_array-like of shape (n_features,) 

p-values of feature scores, None if score_func returned scores only.      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 continuous 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.  
SelectFdr


Select features based on an estimated false discovery rate.  
SelectFwe


Select features based on family-wise error rate.    Examples >>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.feature_selection import GenericUnivariateSelect, chi2
>>> X, y = load_breast_cancer(return_X_y=True)
>>> X.shape
(569, 30)
>>> transformer = GenericUnivariateSelect(chi2, mode='k_best', param=20)
>>> X_new = transformer.fit_transform(X, y)
>>> X_new.shape
(569, 20)
 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 
 
Xarray-like of shape (n_samples, n_features) 

The training input samples.  
yarray-like 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 
 
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_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 
 
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, 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(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(**params) [source]
 
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). 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 
 
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.