sklearn.feature_selection.RFECV

class sklearn.feature_selection.RFECV(estimator, step=1, min_features_to_select=1, cv=’warn’, scoring=None, verbose=0, n_jobs=None) [source]

Feature ranking with recursive feature elimination and cross-validated selection of the best number of features.

Read more in the User Guide.

Parameters:

estimator : object 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. 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. Note that the last iteration may remove fewer than step features in order to reach min_features_to_select. min_features_to_select : int, (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. cv : int, cross-validation generator or an iterable, optional Determines the cross-validation splitting strategy. Possible inputs for cv are: None, to use the default 3-fold cross-validation, integer, to specify the number of folds. An object to be used as a cross-validation generator. An iterable yielding train/test splits. For integer/None inputs, if y is binary or multiclass, sklearn.model_selection.StratifiedKFold is used. If the estimator is a classifier or if y is neither binary nor multiclass, sklearn.model_selection.KFold is used. Refer User Guide for the various cross-validation strategies that can be used here. Changed in version 0.20: cv default value of None will change from 3-fold to 5-fold in v0.22. scoring : string, callable or None, optional, (default=None) A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y). verbose : int, (default=0) Controls verbosity of output. n_jobs : int or None, optional (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.

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. grid_scores_ : array of shape [n_subsets_of_features] The cross-validation scores such that grid_scores_[i] corresponds to the CV score of the i-th subset of features. estimator_ : object The external estimator fit on the reduced dataset.

See also

RFE

Recursive feature elimination

Notes

The size of grid_scores_ is equal to ceil((n_features - min_features_to_select) / step) + 1, where step is the number of features removed at each iteration.

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_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 then predict using the underlying estimator.
predict_log_proba(X)	Predict class log-probabilities for X.
predict_proba(X)	Predict class probabilities for X.
score(X, y)	Reduce X to the selected features and then return the score of the underlying estimator.
set_params(**params)	Set the parameters of this estimator.
transform(X)	Reduce X to the selected features.

__init__(estimator, step=1, min_features_to_select=1, cv=’warn’, scoring=None, verbose=0, n_jobs=None) [source]

decision_function(X) [source]

Compute the decision function of X.

Parameters:	X : array-like or sparse matrix, 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:	score : array, 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}, 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-like, shape = [n_samples] Target values (integers for classification, real numbers for regression). groups : array-like, shape = [n_samples], optional Group labels for the samples used while splitting the dataset into train/test set.

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:	X : numpy array of shape [n_samples, n_features] Training set. y : numpy array of shape [n_samples] Target values.
Returns:	X_new : numpy array of shape [n_samples, n_features_new] Transformed array.

get_params(deep=True) [source]

Get parameters for this estimator.

Parameters:	deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : mapping of string to any Parameter names mapped to their values.

get_support(indices=False) [source]

Get a mask, or integer index, of the features selected

Parameters:	indices : boolean (default False) If True, the return value will be an array of integers, rather than a boolean mask.
Returns:	support : array 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:	X : array of shape [n_samples, n_selected_features] The input samples.
Returns:	X_r : array 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 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.

predict_log_proba(X) [source]

Predict class log-probabilities for X.

Parameters:	X : array of shape [n_samples, n_features] The input samples.
Returns:	p : array 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, 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:	p : array 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) [source]

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) [source]

Set the parameters of this estimator.

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

Returns:	self

transform(X) [source]

Reduce X to the selected features.

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 selected features.

Examples using sklearn.feature_selection.RFECV