6.13.2. scikits.learn.feature_selection.rfe.RFECV¶
- class scikits.learn.feature_selection.rfe.RFECV(estimator=None, n_features=None, percentage=0.1, loss_func=None)¶
Feature ranking with Recursive feature elimination and cross validation
Parameters : estimator : object
A supervised learning estimator with a fit method that updates a coef_ attributes that holds the fitted parameters. The first dimension of the coef_ array must be equal n_features an important features must yield high absolute values in the coef_ array.
For instance this is the case for most supervised learning algorithms such as Support Vector Classifiers and Generalized Linear Models from the svm and linear_model package.
n_features : int
Number of features to select
percentage : float
The percentage of features to remove at each iteration Should be between (0, 1]. By default 0.1 will be taken.
References
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Mach. Learn., 46(1-3), 389–422.
Examples
>>> # TODO!
Attributes
support_ array-like, shape = [n_features] Mask of estimated support ranking_ array-like, shape = [n_features] Mask of the ranking of features Methods
fit(X, y) self Fit the model transform(X) array Reduce X to support - __init__(estimator=None, n_features=None, percentage=0.1, loss_func=None)¶
- fit(X, y, cv=None)¶
Fit the RFE model according to the given training data and parameters.
The final size of the support is tuned by cross validation.
Parameters : X : array-like, shape = [n_samples, n_features]
Training vector, where n_samples in the number of samples and n_features is the number of features.
y : array, shape = [n_samples]
Target values (integers in classification, real numbers in regression)
cv : cross-validation instance
- transform(X, copy=True)¶
Reduce X to the features selected during the fit
Parameters : X : array-like, shape = [n_samples, n_features]
Vector, where n_samples in the number of samples and n_features is the number of features.