RFECV

class ibex.sklearn.feature_selection.RFECV(estimator, step=1, cv=None, scoring=None, verbose=0, n_jobs=1)

Bases: sklearn.feature_selection.rfe.RFECV, ibex._base.FrameMixin

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Feature ranking with recursive feature elimination and cross-validated

selection of the best number of features.

Read more in the User Guide.

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.
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.

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, default 1
Number of cores to run in parallel while fitting across folds. Defaults to 1 core. If n_jobs=-1, then number of jobs is set to number of cores.
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.

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

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])
[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.
decision_function(X)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

fit(X, y)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit the RFE model and automatically tune the number of selected
features.
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).
fit_transform(X, y=None, **fit_params)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

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

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Reverse the transformation operation

X : array of shape [n_samples, n_selected_features]
The input samples.
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)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Reduce X to the selected features and then predict using the
underlying estimator.
X : array of shape [n_samples, n_features]
The input samples.
y : array of shape [n_samples]
The predicted target values.
predict_log_proba(X)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

predict_proba(X)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

score(X, y)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Reduce X to the selected features and then return the score of the
underlying estimator.
X : array of shape [n_samples, n_features]
The input samples.
y : array of shape [n_samples]
The target values.
transform(X)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Reduce X to the selected features.

X : array of shape [n_samples, n_features]
The input samples.
X_r : array of shape [n_samples, n_selected_features]
The input samples with only the selected features.