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 weights are assigned to each one of them. Then, features whose absolute weights are the smallest are pruned from the current set 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 For instance, this is the case for most supervised learning algorithms such as Support Vector Classifiers and Generalized Linear Models from the n_features_to_select : int or None (default=None) The number of features to select. If step : int or float, optional (default=1) If greater than or equal to 1, then verbose : int, default=0 Controls verbosity of output. |
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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 estimator_ : object The external estimator fit on the reduced dataset. |
[R167] | 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. |
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], dtype=bool) >>> selector.ranking_ array([1, 1, 1, 1, 1, 6, 4, 3, 2, 5])
decision_function (*args, **kwargs) | |
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 (*args, **kwargs) | Reduce X to the selected features and then predict using the underlying estimator. |
predict_log_proba (*args, **kwargs) | |
predict_proba (*args, **kwargs) | |
score (*args, **kwargs) | 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]
fit(X, y)
[source]
Parameters: |
X : {array-like, sparse matrix}, shape = [n_samples, n_features] The training input samples. y : array-like, shape = [n_samples] The target values. |
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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. |
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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. |
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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. |
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Returns: |
support : array An index that selects the retained features from a feature vector. If |
inverse_transform(X)
[source]
Reverse the transformation operation
Parameters: |
X : array of shape [n_samples, n_selected_features] The input samples. |
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Returns: |
X_r : array of shape [n_samples, n_original_features]
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predict(*args, **kwargs)
[source]
Parameters: |
X : array of shape [n_samples, n_features] The input samples. |
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Returns: |
y : array of shape [n_samples] The predicted target values. |
score(*args, **kwargs)
[source]
Parameters: |
X : array of shape [n_samples, n_features] The input samples. y : array of shape [n_samples] The target values. |
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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 : |
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transform(X)
[source]
Reduce X to the selected features.
Parameters: |
X : array of shape [n_samples, n_features] The input samples. |
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Returns: |
X_r : array of shape [n_samples, n_selected_features] The input samples with only the selected features. |
sklearn.feature_selection.RFE
© 2007–2016 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html