sklearn.feature_selection.RFE

class sklearn.feature_selection.RFE(estimator, n_features_to_select=None, step=1, verbose=0) [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 a coef_ attribute or through a feature_importances_ attribute. 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 : 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. n_features_to_select : int or None (default=None) The number of features to select. If None, half of the features are selected. 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. verbose : int, (default=0) Controls verbosity of output.

Attributes:

n_features_ : int The number of selected features. 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. estimator_ : object The external estimator fit on the reduced dataset.

See also

RFECV

Recursive feature elimination with built-in cross-validated selection of the best number of features

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 right 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, 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

decision_function(X)	Compute the decision function of X.
fit(X, y)	Fit the RFE model and then the underlying estimator on the 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, n_features_to_select=None, step=1, verbose=0) [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) [source]

Fit the RFE model and then the underlying estimator on the selected

features.

Parameters:	X : {array-like, sparse matrix}, shape = [n_samples, n_features] The training input samples. y : array-like, shape = [n_samples] The target values.

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.RFE