sklearn.feature_selection
.RFECV¶

class
sklearn.feature_selection.
RFECV
(estimator, *, step=1, min_features_to_select=1, cv=None, scoring=None, verbose=0, n_jobs=None, importance_getter='auto')[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
Estimator
instance A supervised learning estimator with a
fit
method that provides information about feature importance either through acoef_
attribute or through afeature_importances_
attribute. 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), 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_selectint, 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
.New in version 0.20.
 cvint, crossvalidation generator or an iterable, default=None
Determines the crossvalidation splitting strategy. Possible inputs for cv are:
None, to use the default 5fold crossvalidation,
integer, to specify the number of folds.
An iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs, if
y
is binary or multiclass,StratifiedKFold
is used. If the estimator is a classifier or ify
is neither binary nor multiclass,KFold
is used.Refer User Guide for the various crossvalidation strategies that can be used here.
Changed in version 0.22:
cv
default value of None changed from 3fold to 5fold. scoringstring, callable or None, default=None
A string (see model evaluation documentation) or a scorer callable object / function with signature
scorer(estimator, X, y)
. verboseint, default=0
Controls verbosity of output.
 n_jobsint or None, 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.New in version 0.18.
 importance_getterstr or callable, default=’auto’
If ‘auto’, uses the feature importance either through a
coef_
orfeature_importances_
attributes of estimator.Also accepts a string that specifies an attribute name/path for extracting feature importance. For example, give
regressor_.coef_
in case ofTransformedTargetRegressor
ornamed_steps.clf.feature_importances_
in case ofPipeline
with its last step namedclf
.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.
 estimator
 Attributes
 estimator_
Estimator
instance The fitted estimator used to select features.
 grid_scores_ndarray 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. n_features_int
The number of selected features with crossvalidation.
 ranking_narray 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. support_ndarray of shape (n_features,)
The mask of selected features.
 estimator_
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.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(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
Compute the decision function of
X
.fit
(X, y[, groups])Fit the RFE model and automatically tune the number of 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
Reverse the transformation operation
predict
(X)Reduce X to the selected features and then predict using the
Predict class logprobabilities for 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{arraylike 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 sparsecsr_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, groups=None)[source]¶  Fit the RFE model and automatically tune the number of selected
features.
 Parameters
 X{arraylike, sparse matrix} of shape (n_samples, n_features)
Training vector, where
n_samples
is the number of samples andn_features
is the total number of features. yarraylike of shape (n_samples,)
Target values (integers for classification, real numbers for regression).
 groupsarraylike of shape (n_samples,) or None, default=None
Group labels for the samples used while splitting the dataset into train/test set. Only used in conjunction with a “Group” cv instance (e.g.,
GroupKFold
).New in version 0.20.

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{arraylike, sparse matrix, dataframe} of shape (n_samples, n_features)
Input samples.
 yndarray of shape (n_samples,), 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
 paramsmapping 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
 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. Ifindices
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 bytransform
.

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 logprobabilities for X.
 Parameters
 Xarray of shape [n_samples, n_features]
The input samples.
 Returns
 parray 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
(X)[source]¶ Predict class probabilities for X.
 Parameters
 X{arraylike 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 sparsecsr_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 pipelines). 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
 selfobject
Estimator instance.