Class DecisionTreeRegressorScikitsLearnNode

A decision tree regressor.

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

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

**Parameters**

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, which is equal to
    variance reduction as feature selection criterion.

splitter : string, optional (default="best")
    The strategy used to choose the split at each node. Supported
    strategies are "best" to choose the best split and "random" to choose
    the best random split.

max_features : int, float, string or None, optional (default=None)
    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.

max_depth : int 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.

min_samples_split : int, optional (default=2)
    The minimum number of samples required to split an internal node.

min_samples_leaf : int, optional (default=1)
    The minimum number of samples required to be at a leaf node.

min_weight_fraction_leaf : float, optional (default=0.)
    The minimum weighted fraction of the input samples required to be at a
    leaf node.

max_leaf_nodes : int or None, optional (default=None)
    Grow a tree 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.

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`.

presort : bool, optional (default=False)
    Whether to presort the data to speed up the finding of best splits in
    fitting. For the default settings of a decision tree on large
    datasets, setting this to true may slow down the training process.
    When using either a smaller dataset or a restricted depth, this may
    speed up the training.

**Attributes**

``feature_importances_`` : array of shape = [n_features]
    The feature importances.
    The higher, the more important the feature.
    The importance of a feature is computed as the
    (normalized) total reduction of the criterion brought
    by that feature. It is also known as the Gini importance [4]_.

``max_features_`` : int,
    The inferred value of max_features.

``n_features_`` : int
    The number of features when ``fit`` is performed.

``n_outputs_`` : int
    The number of outputs when ``fit`` is performed.

``tree_`` : Tree object
    The underlying Tree object.

See also

DecisionTreeClassifier

**References**


.. [1] http://en.wikipedia.org/wiki/Decision_tree_learning

.. [2] L. Breiman, J. Friedman, R. Olshen, and C. Stone, "Classification
       and Regression Trees", Wadsworth, Belmont, CA, 1984.

.. [3] T. Hastie, R. Tibshirani and J. Friedman. "Elements of Statistical
       Learning", Springer, 2009.

.. [4] L. Breiman, and A. Cutler, "Random Forests",
       http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm

**Examples**

>>> from sklearn.datasets import load_boston
>>> from sklearn.cross_validation import cross_val_score
>>> from sklearn.tree import DecisionTreeRegressor
>>> boston = load_boston()
>>> regressor = DecisionTreeRegressor(random_state=0)
>>> cross_val_score(regressor, boston.data, boston.target, cv=10)
...                    # doctest: +SKIP
...
array([ 0.61..., 0.57..., -0.34..., 0.41..., 0.75...,
        0.07..., 0.29..., 0.33..., -1.42..., -1.77...])

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.DecisionTreeRegressor`` 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.DecisionTreeRegressor 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