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.
Estimator instance
A supervised learning estimator with a fit method that provides information about feature importance (e.g. coef_, feature_importances_).
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.
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.
Controls verbosity of output.
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.
Added in version 0.24.
classes_ndarray of shape (n_classes,)
Classes labels available when estimator is a classifier.
Estimator instance
The fitted estimator used to select features.
The number of selected features.
Number of features seen during fit. Only defined if the underlying estimator exposes such an attribute when fit.
Added in version 0.24.
n_features_in_,)
Names of features seen during fit. Defined only when X has feature names that are all strings.
Added in version 1.0.
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.
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])
Compute the decision function of X.
The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.
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 the RFE model and then the underlying estimator on the selected features.
The training input samples.
The target values.
If enable_metadata_routing=False (default): Parameters directly passed to the fit method of the underlying estimator.
If enable_metadata_routing=True: Parameters safely routed to the fit method of the underlying estimator.
Changed in version 1.6: See Metadata Routing User Guide for more details.
Fitted estimator.
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
Input samples.
Target values (None for unsupervised transformations).
Additional fit parameters.
Transformed array.
Mask feature names according to selected features.
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.
Transformed feature names.
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Added in version 1.6.
A MetadataRouter encapsulating routing information.
Get parameters for this estimator.
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Parameter names mapped to their values.
Get a mask, or integer index, of the features selected.
If True, the return value will be an array of integers, rather than a boolean mask.
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.
Reverse the transformation operation.
The input samples.
X with columns of zeros inserted where features would have been removed by transform.
Reduce X to the selected features and predict using the estimator.
The input samples.
Parameters to route to the predict method of the underlying estimator.
Added in version 1.6: Only available if enable_metadata_routing=True, which can be set by using sklearn.set_config(enable_metadata_routing=True). See Metadata Routing User Guide for more details.
The predicted target values.
Predict class log-probabilities for X.
The input samples.
The class log-probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.
Predict class probabilities for X.
The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.
The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.
Reduce X to the selected features and return the score of the estimator.
The input samples.
The target values.
If enable_metadata_routing=False (default): Parameters directly passed to the score method of the underlying estimator.
If enable_metadata_routing=True: Parameters safely routed to the score method of the underlying estimator.
Added in version 1.0.
Changed in version 1.6: See Metadata Routing User Guide for more details.
Score of the underlying base estimator computed with the selected features returned by rfe.transform(X) and y.
Set output container.
See Introducing the set_output API for an example on how to use the API.
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.
Estimator instance.
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.
Estimator parameters.
Estimator instance.
Reduce X to the selected features.
The input samples.
The input samples with only the selected features.
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