sklearn.feature_selection
.RFE¶
- class sklearn.feature_selection.RFE(estimator, *, n_features_to_select=None, step=1, verbose=0, importance_getter='auto')[source]¶
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.
- Parameters:
- estimator
Estimator
instance A supervised learning estimator with a
fit
method that provides information about feature importance (e.g.coef_
,feature_importances_
).- n_features_to_selectint or float, default=None
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.
- 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.- verboseint, default=0
Controls verbosity of output.
- 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 (implemented with
attrgetter
). For example, giveregressor_.coef_
in case ofTransformedTargetRegressor
ornamed_steps.clf.feature_importances_
in case of class:~sklearn.pipeline.Pipeline
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:
classes_
ndarray of shape (n_classes,)Classes labels available when
estimator
is a classifier.- estimator_
Estimator
instance The fitted estimator used to select features.
- n_features_int
The number of selected features.
- 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_ndarray 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
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])
Methods
Compute the decision function of
X
.fit
(X, y, **fit_params)Fit the RFE model and then the underlying estimator on the 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.
Raise
NotImplementedError
.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 predict using the estimator.
Predict class log-probabilities for 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_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)[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 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, **fit_params)[source]¶
Fit the RFE model and then the underlying estimator on the selected features.
- Parameters:
- X{array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples.
- yarray-like of shape (n_samples,)
The target values.
- **fit_paramsdict
Additional parameters passed to the
fit
method of the underlying estimator.
- Returns:
- selfobject
Fitted estimator.
- fit_transform(X, y=None, **fit_params)[source]¶
Fit to data, then transform it.
Fits transformer to
X
andy
with optional parametersfit_params
and returns a transformed version ofX
.- 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
isNone
, thenfeature_names_in_
is used as feature names in. Iffeature_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, theninput_features
must matchfeature_names_in_
iffeature_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. 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 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 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, **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.New in version 1.0.
- Returns:
- scorefloat
Score of the underlying base estimator computed with the selected features returned by
rfe.transform(X)
andy
.
- 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”}, default=None
Configure output of
transform
andfit_transform
."default"
: Default output format of a transformer"pandas"
: DataFrame output"polars"
: Polars outputNone
: Transform configuration is unchanged
New 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.