Class DecisionTreeClassifierScikitsLearnNode

A decision tree classifier.

This node has been automatically generated by wrapping the ``sklearn.tree.tree.DecisionTreeClassifier`` 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="gini")
    The function to measure the quality of a split. Supported criteria are
    "gini" for the Gini impurity and "entropy" for the information gain.

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=sqrt(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.

class_weight : dict, list of dicts, "balanced" or None, optional (default=None)
    Weights associated with classes in the form ``{class_label: weight}``.
    If not given, all classes are supposed to have weight one. For
    multi-output problems, a list of dicts can be provided in the same
    order as the columns of y.

    The "balanced" mode uses the values of y to automatically adjust
    weights inversely proportional to class frequencies in the input data
    as ``n_samples / (n_classes * np.bincount(y))``

    For multi-output, the weights of each column of y will be multiplied.

    Note that these weights will be multiplied with sample_weight (passed
    through the fit method) if sample_weight is specified.

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**

``classes_`` : array of shape = [n_classes] or a list of such arrays
    The classes labels (single output problem),
    or a list of arrays of class labels (multi-output problem).

``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_classes_`` : int or list
    The number of classes (for single output problems),
    or a list containing the number of classes for each
    output (for multi-output problems).

``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

DecisionTreeRegressor

**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_iris
>>> from sklearn.cross_validation import cross_val_score
>>> from sklearn.tree import DecisionTreeClassifier
>>> clf = DecisionTreeClassifier(random_state=0)
>>> iris = load_iris()
>>> cross_val_score(clf, iris.data, iris.target, cv=10)
...                             # doctest: +SKIP
...
array([ 1.     ,  0.93...,  0.86...,  0.93...,  0.93...,
        0.93...,  0.93...,  1.     ,  0.93...,  1.      ])

Overrides: object.__init__

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Predict class or regression value for X.

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

For a classification model, the predicted class for each sample in X is returned. For a regression model, the predicted value based on X is returned.

Parameters

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

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

check_input : boolean, (default=True)

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

Returns

y : array of shape = [n_samples] or [n_samples, n_outputs]

The predicted classes, or the predict values.

Overrides: ClassifierNode.label

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

This node has been automatically generated by wrapping the sklearn.tree.tree.DecisionTreeClassifier 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