Class ExtraTreeRegressorScikitsLearnNode

An extremely randomized tree regressor.

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

Extra-trees differ from classic decision trees in the way they are built.
When looking for the best split to separate the samples of a node into two
groups, random splits are drawn for each of the `max_features` randomly
selected features and the best split among those is chosen. When
`max_features` is set 1, this amounts to building a totally random
decision tree.

Warning: Extra-trees should only be used within ensemble methods.

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

See also

ExtraTreeClassifier, ExtraTreesClassifier, ExtraTreesRegressor

**References**


.. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees",
       Machine Learning, 63(1), 3-42, 2006.

Overrides: object.__init__

DEPRECATED: Support to use estimators as feature selectors will be removed in version 0.19. Use SelectFromModel instead.

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

Reduce X to its most important features.

        Uses ``coef_`` or ``feature_importances_`` to determine the most
        important features.  For models with a ``coef_`` for each class, the
        absolute sum over the classes is used.

        Parameters
        ----------
        X : array or scipy sparse matrix of shape [n_samples, n_features]
            The input samples.

        threshold : string, float or None, 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
            available, the object attribute ``threshold`` is used. Otherwise,
            "mean" is used by default.

        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

Build a decision tree from the training set (X, y).

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

Parameters

X : array-like or sparse matrix, shape = [n_samples, n_features]

The training input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csc_matrix.

y : array-like, shape = [n_samples] or [n_samples, n_outputs]

The target values (class labels in classification, real numbers in regression). In the regression case, use dtype=np.float64 and order='C' for maximum efficiency.

sample_weight : array-like, shape = [n_samples] or None

Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. In the case of classification, splits are also ignored if they would result in any single class carrying a negative weight in either child node.

check_input : boolean, (default=True)

Allow to bypass several input checking. Don't use this parameter unless you know what you do.

X_idx_sorted : array-like, shape = [n_samples, n_features], optional

The indexes of the sorted training input samples. If many tree are grown on the same dataset, this allows the ordering to be cached between trees. If None, the data will be sorted here. Don't use this parameter unless you know what to do.

Returns

self : object

Returns self.

Overrides: Node.stop_training