Class RFEScikitsLearnNode

Feature ranking with recursive feature elimination.

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

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 :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.

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), then `step` corresponds to the percentage
    (rounded down) of features to remove at each iteration.

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.

``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.

``estimator_`` : object
    The external estimator fit on the reduced dataset.

**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_ # 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.RFE 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 then the underlying estimator on the selected features.

This node has been automatically generated by wrapping the sklearn.feature_selection.rfe.RFE 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]

The training input samples.

y : array-like, shape = [n_samples]

The target values.

Overrides: Node.stop_training