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]#

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.

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.

&gt;&gt;&gt; from sklearn.datasets import make_friedman1
&gt;&gt;&gt; from sklearn.feature_selection import RFECV
&gt;&gt;&gt; from sklearn.svm import SVR
&gt;&gt;&gt; X, y = make_friedman1(n_samples=50, n_features=10, random_state=0)
&gt;&gt;&gt; estimator = SVR(kernel="linear")
&gt;&gt;&gt; selector = RFECV(estimator, step=1, cv=5)
&gt;&gt;&gt; selector = selector.fit(X, y)
&gt;&gt;&gt; selector.support_
array([ True,  True,  True,  True,  True, False, False, False, False,
       False])
&gt;&gt;&gt; selector.ranking_
array([1, 1, 1, 1, 1, 6, 4, 3, 2, 5])

property classes_#

Classes labels available when estimator is a classifier.

Returns:

ndarray of shape (n_classes,)

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, groups=None)[source]#

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).

Added in version 0.20.

Returns:

selfobject: Fitted estimator.

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_feature_names_out(input_features=None)[source]#

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()[source]#

Raise NotImplementedError.

This estimator does not support metadata routing yet.

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 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)[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, **fit_params)[source]#

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.

Added 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[source]#

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.

Added 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)[source]#

Set output container.

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

Parameters:

transform{“default”, “pandas”, “polars”}, 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

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

Returns:

selfestimator instance: Estimator instance.

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.

gallery examples#

Recursive feature elimination with cross-validation