sklearn.feature_selection.RFE  
class sklearn.feature_selection.RFE(estimator, *, n_features_to_select=None, step=1, verbose=0, importance_getter='auto') [source]
 
Feature ranking with recursive feature elimination. Given an external estimator that assigns weights to features (e.g., the coefficients of a linear model), the goal of recursive feature elimination (RFE) is to select features by recursively considering smaller and smaller sets of features. First, the estimator is trained on the initial set of features and the importance of each feature is obtained either through any specific attribute or callable. Then, the least important features are pruned from current set of features. That procedure is recursively repeated on the pruned set until the desired number of features to select is eventually reached. Read more in the User Guide.  Parameters 
 
estimatorEstimator instance 

A supervised learning estimator with a fit method that provides information about feature importance (e.g. coef_, feature_importances_).  
n_features_to_selectint or float, default=None 

The number of features to select. If None, half of the features are selected. If integer, the parameter is the absolute number of features to select. If float between 0 and 1, it is the fraction of features to select.  Changed in version 0.24: Added float values for fractions.   
stepint or float, default=1 

If greater than or equal to 1, then step corresponds to the (integer) number of features to remove at each iteration. If within (0.0, 1.0), then step corresponds to the percentage (rounded down) of features to remove at each iteration.  
verboseint, default=0 

Controls verbosity of output.  
importance_getterstr or callable, default=’auto’ 

If ‘auto’, uses the feature importance either through a coef_ or feature_importances_ attributes of estimator. Also accepts a string that specifies an attribute name/path for extracting feature importance (implemented with attrgetter). For example, give regressor_.coef_ in case of TransformedTargetRegressor or named_steps.clf.feature_importances_ in case of class:~sklearn.pipeline.Pipeline with its last step named clf. If callable, overrides the default feature importance getter. The callable is passed with the fitted estimator and it should return importance for each feature.  New in version 0.24.     Attributes 
 
estimator_Estimator instance 

The fitted estimator used to select features.  
n_features_int 

The number of selected features.  
ranking_ndarray of shape (n_features,) 

The feature ranking, such that ranking_[i] corresponds to the ranking position of the i-th feature. Selected (i.e., estimated best) features are assigned rank 1.  
support_ndarray of shape (n_features,) 

The mask of selected features.      See also  
RFECV


Recursive feature elimination with built-in cross-validated selection of the best number of features.  
SelectFromModel


Feature selection based on thresholds of importance weights.  
SequentialFeatureSelector


Sequential cross-validation based feature selection. Does not rely on importance weights.    Notes Allows NaN/Inf in the input if the underlying estimator does as well. References  
1  
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V., “Gene selection for cancer classification using support vector machines”, Mach. Learn., 46(1-3), 389–422, 2002.   Examples The following example shows how to retrieve the 5 most informative features in the Friedman #1 dataset. >>> from sklearn.datasets import make_friedman1
>>> from sklearn.feature_selection import RFE
>>> from sklearn.svm import SVR
>>> X, y = make_friedman1(n_samples=50, n_features=10, random_state=0)
>>> estimator = SVR(kernel="linear")
>>> selector = RFE(estimator, n_features_to_select=5, step=1)
>>> selector = selector.fit(X, y)
>>> selector.support_
array([ True,  True,  True,  True,  True, False, False, False, False,
       False])
>>> selector.ranking_
array([1, 1, 1, 1, 1, 6, 4, 3, 2, 5])
 Methods  
decision_function(X) Compute the decision function of X.  
fit(X, y) Fit the RFE model and then the underlying estimator on the selected  
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  
predict(X) Reduce X to the selected features and then predict using the  
predict_log_proba(X) Predict class log-probabilities for X.  
predict_proba(X) Predict class probabilities for X.  
score(X, y) Reduce X to the selected features and then return the score of the  
set_params(**params) Set the parameters of this estimator.  
transform(X) Reduce X to the selected features.    
decision_function(X) [source]
 
Compute the decision function of X.  Parameters 
 
X{array-like or sparse matrix} of shape (n_samples, n_features) 

The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.    Returns 
 
scorearray, shape = [n_samples, n_classes] or [n_samples] 

The decision function of the input samples. The order of the classes corresponds to that in the attribute classes_. Regression and binary classification produce an array of shape [n_samples].     
  
fit(X, y) [source]
 
 Fit the RFE model and then the underlying estimator on the selected

features.    Parameters 
 
X{array-like, sparse matrix} of shape (n_samples, n_features) 

The training input samples.  
yarray-like of shape (n_samples,) 

The target values.     
  
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.     
  
predict(X) [source]
 
 Reduce X to the selected features and then predict using the

underlying estimator.    Parameters 
 
Xarray of shape [n_samples, n_features] 

The input samples.    Returns 
 
yarray of shape [n_samples] 

The predicted target values.     
  
predict_log_proba(X) [source]
 
Predict class log-probabilities for X.  Parameters 
 
Xarray of shape [n_samples, n_features] 

The input samples.    Returns 
 
parray of shape (n_samples, n_classes) 

The class log-probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.     
  
predict_proba(X) [source]
 
Predict class probabilities for X.  Parameters 
 
X{array-like or sparse matrix} of shape (n_samples, n_features) 

The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.    Returns 
 
parray of shape (n_samples, n_classes) 

The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.     
  
score(X, y) [source]
 
 Reduce X to the selected features and then return the score of the

underlying estimator.    Parameters 
 
Xarray of shape [n_samples, n_features] 

The input samples.  
yarray of shape [n_samples] 

The target values.     
  
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.     
 
 Examples using sklearn.feature_selection.RFE
 
  Recursive feature elimination