Class ExtraTreesRegressorScikitsLearnNode

An extra-trees regressor.

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

This class implements a meta estimator that fits a number of
randomized decision trees (a.k.a. extra-trees) on various sub-samples
of the dataset and use averaging to improve the predictive accuracy
and control over-fitting.

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

**Parameters**

n_estimators : integer, optional (default=10)
    The number of trees in the forest.

criterion : string, optional (default="mse")
    The function to measure the quality of a split. The only supported
    criterion is "mse" for the mean squared error.
    Note: this parameter is tree-specific.

max_features : int, float, string or None, optional (default="auto")
    The number of features to consider when looking for the best split:


    - If int, then consider `max_features` features at each split.
    - If float, then `max_features` is a percentage and
      `int(max_features * n_features)` features are considered at each
      split.
    - If "auto", then `max_features=n_features`.
    - If "sqrt", then `max_features=sqrt(n_features)`.
    - If "log2", then `max_features=log2(n_features)`.
    - If None, then `max_features=n_features`.

    Note: the search for a split does not stop until at least one
    valid partition of the node samples is found, even if it requires to
    effectively inspect more than ``max_features`` features.
    Note: this parameter is tree-specific.

max_depth : integer or None, optional (default=None)
    The maximum depth of the tree. If None, then nodes are expanded until
    all leaves are pure or until all leaves contain less than
    min_samples_split samples.
    Ignored if ``max_leaf_nodes`` is not None.
    Note: this parameter is tree-specific.

min_samples_split : integer, optional (default=2)
    The minimum number of samples required to split an internal node.
    Note: this parameter is tree-specific.

min_samples_leaf : integer, optional (default=1)
    The minimum number of samples in newly created leaves.  A split is
    discarded if after the split, one of the leaves would contain less then
    ``min_samples_leaf`` samples.
    Note: this parameter is tree-specific.

min_weight_fraction_leaf : float, optional (default=0.)
    The minimum weighted fraction of the input samples required to be at a
    leaf node.
    Note: this parameter is tree-specific.

max_leaf_nodes : int or None, optional (default=None)
    Grow trees with ``max_leaf_nodes`` in best-first fashion.
    Best nodes are defined as relative reduction in impurity.
    If None then unlimited number of leaf nodes.
    If not None then ``max_depth`` will be ignored.
    Note: this parameter is tree-specific.

bootstrap : boolean, optional (default=False)
    Whether bootstrap samples are used when building trees.
    Note: this parameter is tree-specific.

oob_score : bool
    Whether to use out-of-bag samples to estimate
    the generalization error.

n_jobs : integer, optional (default=1)
    The number of jobs to run in parallel for both `fit` and `predict`.
    If -1, then the number of jobs is set to the number of cores.

random_state : int, RandomState instance or None, optional (default=None)
    If int, random_state is the seed used by the random number generator;
    If RandomState instance, random_state is the random number generator;
    If None, the random number generator is the RandomState instance used
    by `np.random`.

verbose : int, optional (default=0)
    Controls the verbosity of the tree building process.

warm_start : bool, optional (default=False)
    When set to ``True``, reuse the solution of the previous call to fit
    and add more estimators to the ensemble, otherwise, just fit a whole
    new forest.

**Attributes**

``estimators_`` : list of DecisionTreeRegressor
    The collection of fitted sub-estimators.

``feature_importances_`` : array of shape = [n_features]
    The feature importances (the higher, the more important the feature).

``n_features_`` : int
    The number of features.

``n_outputs_`` : int
    The number of outputs.

``oob_score_`` : float
    Score of the training dataset obtained using an out-of-bag estimate.

``oob_prediction_`` : array of shape = [n_samples]
    Prediction computed with out-of-bag estimate on the training set.

**References**


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

See also

sklearn.tree.ExtraTreeRegressor: Base estimator for this ensemble.
RandomForestRegressor: Ensemble regressor using trees with optimal splits.

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.ensemble.forest.ExtraTreesRegressor`` 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 forest of trees from the training set (X, y).

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

Parameters

X : array-like or sparse matrix of 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).

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.

Returns

self : object

Returns self.

Overrides: Node.stop_training