8.8.7. sklearn.feature_selection.RFECV

class sklearn.feature_selection.RFECV(estimator, step=1, cv=None, loss_func=None)

Feature ranking with recursive feature elimination and cross-validated

selection of the best number of features.

Parameters :

estimator : object

A supervised learning estimator with a fit method that updates a coef_ attribute that holds the fitted parameters. Important features must correspond to high absolute values in the coef_ array.

For instance, this is the case for most supervised learning algorithms such as Support Vector Classifiers and Generalized Linear Models from the svm and linear_model modules.

step : int or float, optional (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.

cv : int or cross-validation generator, optional (default=None)

If int, it is the number of folds. If None, 3-fold cross-validation is performed by default. Specific cross-validation objects can also be passed, see scikits.learn.cross_validation module for details.

loss_function : function, optional (default=None)

The loss function to minimize by cross-validation. If None, then the score function of the estimator is maximized.

Notes

References:

[R58]

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], dtype=bool)
>>> selector.ranking_
array([1, 1, 1, 1, 1, 6, 4, 3, 2, 5])

Attributes

n_features_	int	The number of selected features with cross-validation.
support_	array of shape [n_features]	The mask of selected features.
ranking_	array 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.
cv_scores_	array of shape [n_subsets_of_features]	The cross-validation scores such that cv_scores_[i] corresponds to the CV score of the i-th subset of features.

Methods

fit(X, y)	Fit the RFE model and automatically tune the number of selected
predict(X)	Reduce X to the selected features and then predict using the
score(X, y)	Reduce X to the selected features and then return the score of the
set_params(**params)	Set the parameters of the estimator.
transform(X)	Reduce X to the selected features during the elimination.

__init__(estimator, step=1, cv=None, loss_func=None)

fit(X, y)

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

Parameters :

X : array 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.

y : array of shape [n_samples]

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

predict(X)

Reduce X to the selected features and then predict using the underlying estimator.

Parameters :

X : array of shape [n_samples, n_features]

The input samples.

Returns :

y : array of shape [n_samples]

The predicted target values.

score(X, y)

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

Parameters :

X : array of shape [n_samples, n_features]

The input samples.

y : array of shape [n_samples]

The target values.

set_params(**params)

Set the parameters of the estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns :self :

transform(X)

Reduce X to the selected features during the elimination.

Parameters :

X : array of shape [n_samples, n_features]

The input samples.

Returns :

X_r : array of shape [n_samples, n_selected_features]

The input samples with only the features selected during the elimination.