sklearn.feature_selection
.RFECV¶

class
sklearn.feature_selection.
RFECV
(estimator, step=1, min_features_to_select=1, cv=’warn’, scoring=None, verbose=0, n_jobs=None)[source]¶ Feature ranking with recursive feature elimination and crossvalidated selection of the best number of features.
See glossary entry for crossvalidation estimator.
Read more in the User Guide.
Parameters:  estimator : object
A supervised learning estimator with a
fit
method that provides information about feature importance either through acoef_
attribute or through afeature_importances_
attribute. step : int or float, optional (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), thenstep
corresponds to the percentage (rounded down) of features to remove at each iteration. Note that the last iteration may remove fewer thanstep
features in order to reachmin_features_to_select
. min_features_to_select : int, (default=1)
The minimum number of features to be selected. This number of features will always be scored, even if the difference between the original feature count and
min_features_to_select
isn’t divisible bystep
. cv : int, crossvalidation generator or an iterable, optional
Determines the crossvalidation splitting strategy. Possible inputs for cv are:
 None, to use the default 3fold crossvalidation,
 integer, to specify the number of folds.
 CV splitter,
 An iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs, if
y
is binary or multiclass,sklearn.model_selection.StratifiedKFold
is used. If the estimator is a classifier or ify
is neither binary nor multiclass,sklearn.model_selection.KFold
is used.Refer User Guide for the various crossvalidation strategies that can be used here.
Changed in version 0.20:
cv
default value of None will change from 3fold to 5fold in v0.22. scoring : string, callable or None, optional, (default=None)
A string (see model evaluation documentation) or a scorer callable object / function with signature
scorer(estimator, X, y)
. verbose : int, (default=0)
Controls verbosity of output.
 n_jobs : int or None, optional (default=None)
Number of cores to run in parallel while fitting across folds.
None
means 1 unless in ajoblib.parallel_backend
context.1
means using all processors. See Glossary for more details.
Attributes:  n_features_ : int
The number of selected features with crossvalidation.
 support_ : array of shape [n_features]
The mask of selected features.
 ranking_ : array of shape [n_features]
The feature ranking, such that
ranking_[i]
corresponds to the ranking position of the ith feature. Selected (i.e., estimated best) features are assigned rank 1. grid_scores_ : array of shape [n_subsets_of_features]
The crossvalidation scores such that
grid_scores_[i]
corresponds to the CV score of the ith subset of features. estimator_ : object
The external estimator fit on the reduced dataset.
See also
RFE
 Recursive feature elimination
Notes
The size of
grid_scores_
is equal toceil((n_features  min_features_to_select) / step) + 1
, where step is the number of features removed at each iteration.References
[R6f4d61ceb4111] Guyon, I., Weston, J., Barnhill, S., & Vapnik, V., “Gene selection for cancer classification using support vector machines”, Mach. Learn., 46(13), 389–422, 2002. Examples
The following example shows how to retrieve the apriori not known 5 informative features in the Friedman #1 dataset.
>>> from sklearn.datasets import make_friedman1 >>> from sklearn.feature_selection import RFECV >>> from sklearn.svm import SVR >>> X, y = make_friedman1(n_samples=50, n_features=10, random_state=0) >>> estimator = SVR(kernel="linear") >>> selector = RFECV(estimator, step=1, cv=5) >>> 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
(self, X)Compute the decision function of X
.fit
(self, X, y[, groups])Fit the RFE model and automatically tune the number of selected features. fit_transform
(self, X[, y])Fit to data, then transform it. get_params
(self[, deep])Get parameters for this estimator. get_support
(self[, indices])Get a mask, or integer index, of the features selected inverse_transform
(self, X)Reverse the transformation operation predict
(self, X)Reduce X to the selected features and then predict using the underlying estimator. predict_log_proba
(self, X)Predict class logprobabilities for X. predict_proba
(self, X)Predict class probabilities for X. score
(self, X, y)Reduce X to the selected features and then return the score of the underlying estimator. set_params
(self, \*\*params)Set the parameters of this estimator. transform
(self, X)Reduce X to the selected features. 
__init__
(self, estimator, step=1, min_features_to_select=1, cv=’warn’, scoring=None, verbose=0, n_jobs=None)[source]¶

decision_function
(self, X)[source]¶ Compute the decision function of
X
.Parameters:  X : arraylike or sparse matrix, 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 sparsecsr_matrix
.
Returns:  score : array, 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
(self, X, y, groups=None)[source]¶  Fit the RFE model and automatically tune the number of selected
 features.
Parameters:  X : {arraylike, sparse matrix}, shape = [n_samples, n_features]
Training vector, where n_samples is the number of samples and n_features is the total number of features.
 y : arraylike, shape = [n_samples]
Target values (integers for classification, real numbers for regression).
 groups : arraylike, shape = [n_samples], optional
Group labels for the samples used while splitting the dataset into train/test set.

fit_transform
(self, 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
(self, 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
(self, 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. Ifindices
is True, this is an integer array of shape [# output features] whose values are indices into the input feature vector.

inverse_transform
(self, X)[source]¶ Reverse the transformation operation
Parameters:  X : array of shape [n_samples, n_selected_features]
The input samples.
Returns:

predict
(self, X)[source]¶  Reduce X to the selected features and then predict using the
 underlying estimator.
Parameters:  X : array of shape [n_samples, n_features]
The input samples.
Returns:  y : array of shape [n_samples]
The predicted target values.

predict_log_proba
(self, X)[source]¶ Predict class logprobabilities for X.
Parameters:  X : array of shape [n_samples, n_features]
The input samples.
Returns:  p : array of shape = [n_samples, n_classes]
The class logprobabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.

predict_proba
(self, X)[source]¶ Predict class probabilities for X.
Parameters:  X : arraylike or sparse matrix, 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 sparsecsr_matrix
.
Returns:  p : array 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
(self, X, y)[source]¶  Reduce X to the selected features and then return the score of the
 underlying estimator.
Parameters:  X : array of shape [n_samples, n_features]
The input samples.
 y : array of shape [n_samples]
The target values.

set_params
(self, **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