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')

Recursive feature elimination with cross-validation to select features.

The number of features selected is tuned automatically by fitting an RFE selector on the different cross-validation splits (provided by the cv parameter). The performance of the RFE selector are evaluated using scorer for different number of selected features and aggregated together. Finally, the scores are averaged across folds and the number of features selected is set to the number of features that maximize the cross-validation score. See glossary entry for cross-validation estimator.

Read more in the User Guide.

Parameters:

estimatorEstimator instance

A supervised learning estimator with a fit method that provides information about feature importance either through a coef_ attribute or through a feature_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), then step corresponds to the percentage (rounded down) of features to remove at each iteration. Note that the last iteration may remove fewer than step features in order to reach min_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 by step.

New in version 0.20.

cvint, cross-validation generator or an iterable, default=None

Determines the cross-validation splitting strategy. Possible inputs for cv are:

• None, to use the default 5-fold cross-validation,

• 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, StratifiedKFold is used. If the estimator is a classifier or if y is neither binary nor multiclass, KFold is used.

Refer User Guide for the various cross-validation strategies that can be used here.

Changed in version 0.22: cv default value of None changed from 3-fold to 5-fold.

scoringstr, 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 a joblib.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_ or feature_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 of TransformedTargetRegressor or named_steps.clf.feature_importances_ in case of 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:

classes_ndarray of shape (n_classes,)

Classes labels available when estimator is a classifier.

estimator_Estimator instance

The fitted estimator used to select features.

cv_results_dict of ndarrays

A dict with keys:

split(k)_test_scorendarray of shape (n_subsets_of_features,)

The cross-validation scores across (k)th fold.

mean_test_scorendarray of shape (n_subsets_of_features,)

Mean of scores over the folds.

std_test_scorendarray of shape (n_subsets_of_features,)

Standard deviation of scores over the folds.

New in version 1.0.

n_features_int

The number of selected features with cross-validation.

n_features_in_int

Number of features seen during fit. Only defined if the underlying estimator exposes such an attribute when fit.

New in version 0.24.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when X has feature names that are all strings.

New in version 1.0.

ranking_narray 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

RFE

Recursive feature elimination.

Notes

The size of all values in cv_results_ is equal to ceil((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(1-3), 389–422, 2002.

Examples

The following example shows how to retrieve the a-priori 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(X)	Compute the decision function of X.
fit(X, y[, groups])	Fit the RFE model and automatically tune the number of selected features.
fit_transform(X[, y])	Fit to data, then transform it.
get_feature_names_out([input_features])	Mask feature names according to selected features.
get_metadata_routing()	Raise NotImplementedError.
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 predict using the estimator.
predict_log_proba(X)	Predict class log-probabilities for X.
predict_proba(X)	Predict class probabilities for X.
score(X, y, **fit_params)	Reduce X to the selected features and return the score of the estimator.
set_fit_request(*[, groups])	Request metadata passed to the fit method.
set_output(*[, transform])	Set output container.
set_params(**params)	Set the parameters of this estimator.
transform(X)	Reduce X to the selected features.

property classes_

Classes labels available when estimator is a classifier.

Returns:

ndarray of shape (n_classes,)

decision_function(X)

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, groups=None)

Fit the RFE model and automatically tune the number of selected features.

Parameters:

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

Training vector, where n_samples is the number of samples and n_features is the total number of features.

yarray-like of shape (n_samples,)

Target values (integers for classification, real numbers for regression).

groupsarray-like 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.

Returns:

selfobject

Fitted estimator.

fit_transform(X, y=None, **fit_params)

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_feature_names_out(input_features=None)

Mask feature names according to selected features.

Parameters:

input_featuresarray-like of str or None, default=None

Input features.

• If input_features is None, then feature_names_in_ is used as feature names in. If feature_names_in_ is not defined, then the following input feature names are generated: ["x0", "x1", ..., "x(n_features_in_ - 1)"].

• If input_features is an array-like, then input_features must match feature_names_in_ if feature_names_in_ is defined.

Returns:

feature_names_outndarray of str objects

Transformed feature names.

get_metadata_routing()

Raise NotImplementedError.

This estimator does not support metadata routing yet.

get_params(deep=True)

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)

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)

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)

Reduce X to the selected features and predict using the 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)

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)

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, **fit_params)

Reduce X to the selected features and return the score of the estimator.

Parameters:

Xarray of shape [n_samples, n_features]

The input samples.

yarray of shape [n_samples]

The target values.

**fit_paramsdict

Parameters to pass to the score method of the underlying estimator.

New in version 1.0.

Returns:

scorefloat

Score of the underlying base estimator computed with the selected features returned by rfe.transform(X) and y.

set_fit_request(*, groups: bool | None | str = '$UNCHANGED$') → RFECV

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to fit.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

groupsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for groups parameter in fit.

Returns:

selfobject

The updated object.

set_output(*, transform=None)

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform{“default”, “pandas”}, default=None

Configure output of transform and fit_transform.

• "default": Default output format of a transformer

• "pandas": DataFrame output

• "polars": Polars output

• None: Transform configuration is unchanged

New in version 1.4: "polars" option was added.

Returns:

selfestimator instance

Estimator instance.

set_params(**params)

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)

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.RFECV

Recursive feature elimination with cross-validation

Recursive feature elimination with cross-validation