Class RFECVScikitsLearnNode

Feature ranking with recursive feature elimination and cross-validated
selection of the best number of features.

This node has been automatically generated by wrapping the ``sklearn.feature_selection.rfe.RFECV`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

Read more in the :ref:`User Guide <rfe>`.

**Parameters**

estimator : object
    A supervised learning estimator with a `fit` method that updates a
    `coef_` attribute that holds the fitted parameters. Important features
    must correspond to 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` modules.

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,
    :class:`StratifiedKFold` used. If the estimator is a classifier
    or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.

    Refer :ref:`User Guide <cross_validation>` 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)``.

estimator_params : dict
    Parameters for the external estimator.
    This attribute is deprecated as of version 0.16 and will be removed in
    0.18. Use estimator initialisation or set_params method instead.

verbose : int, default=0
    Controls verbosity of output.

**Attributes**

``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.

**Notes**

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.

**Examples**

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_ # doctest: +NORMALIZE_WHITESPACE
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])

**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.

Overrides: object.__init__

Reduce X to the selected features.

This node has been automatically generated by wrapping the sklearn.feature_selection.rfe.RFECV class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

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.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Fit the RFE model and automatically tune the number of selected features.

This node has been automatically generated by wrapping the sklearn.feature_selection.rfe.RFECV class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

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).

Overrides: Node.stop_training