Class SelectFromModelScikitsLearnNode

Meta-transformer for selecting features based on importance weights.

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

.. versionadded:: 0.17

**Parameters**

estimator : object
    The base estimator from which the transformer is built.
    This can be both a fitted (if ``prefit`` is set to True)
    or a non-fitted estimator.

threshold : string, float, optional default None
    The threshold value to use for feature selection. Features whose
    importance is greater or equal are kept while the others are
    discarded. If "median" (resp. "mean"), then the ``threshold`` value is
    the median (resp. the mean) of the feature importances. A scaling
    factor (e.g., "1.25*mean") may also be used. If None and if the
    estimator has a parameter penalty set to l1, either explicitly
    or implicity (e.g, Lasso), the threshold is used is 1e-5.
    Otherwise, "mean" is used by default.

prefit : bool, default False
    Whether a prefit model is expected to be passed into the constructor
    directly or not. If True, ``transform`` must be called directly
    and SelectFromModel cannot be used with ``cross_val_score``,
    ``GridSearchCV`` and similar utilities that clone the estimator.
    Otherwise train the model using ``fit`` and then ``transform`` to do
    feature selection.

**Attributes**

`estimator_`: an estimator
    The base estimator from which the transformer is built.
    This is stored only when a non-fitted estimator is passed to the
    ``SelectFromModel``, i.e when prefit is False.

`threshold_`: float
    The threshold value used for feature selection.

Overrides: object.__init__

Reduce X to the selected features.

This node has been automatically generated by wrapping the sklearn.feature_selection.from_model.SelectFromModel 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 SelectFromModel meta-transformer.

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

Parameters

X : array-like of shape (n_samples, n_features)

The training input samples.

y : array-like, shape (n_samples,)

The target values (integers that correspond to classes in classification, real numbers in regression).

**fit_params : Other estimator specific parameters

Returns

self : object

Returns self.

Overrides: Node.stop_training