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 afeature_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 acoef_
attribute or through afeature_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), thenstep
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 ith 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 builtin crossvalidated 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(13), 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 logprobabilities 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. 
decision_function
(X)[source]¶ Compute the decision function of
X
.Parameters:  X : arraylike 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 sparsecsr_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 : {arraylike, sparse matrix}, shape = [n_samples, n_features]
The training input samples.
 y : arraylike, 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 logprobabilities 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 logprobabilities 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 : arraylike 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 sparsecsr_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