Class RandomForestClassifierScikitsLearnNode

A random forest classifier.

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

A random forest is a meta estimator that fits a number of decision tree
classifiers on various sub-samples of the dataset and use averaging to
improve the predictive accuracy and control over-fitting.
The sub-sample size is always the same as the original
input sample size but the samples are drawn with replacement if
`bootstrap=True` (default).

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="gini")
    The function to measure the quality of a split. Supported criteria are
    "gini" for the Gini impurity and "entropy" for the information gain.
    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=sqrt(n_features)`.
    - If "sqrt", then `max_features=sqrt(n_features)` (same as "auto").
    - 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=True)
    Whether bootstrap samples are used when building trees.

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.

class_weight : dict, list of dicts, "balanced", "balanced_subsample" or None, optional

    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))``

    The "balanced_subsample" mode is the same as "balanced" except that weights are
    computed based on the bootstrap sample for every tree grown.

    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.

**Attributes**

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

``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).

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

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

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

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

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

``oob_decision_function_`` : array of shape = [n_samples, n_classes]
    Decision function computed with out-of-bag estimate on the training
    set. If n_estimators is small it might be possible that a data point
    was never left out during the bootstrap. In this case,
    `oob_decision_function_` might contain NaN.

**References**


.. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001.

See also

DecisionTreeClassifier, ExtraTreesClassifier

Overrides: object.__init__

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Predict class for X.

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

The predicted class of an input sample is a vote by the trees in the forest, weighted by their probability estimates. That is, the predicted class is the one with highest mean probability estimate across the trees.

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.

Returns

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

The predicted classes.

Overrides: ClassifierNode.label

Build a forest of trees from the training set (X, y).

This node has been automatically generated by wrapping the sklearn.ensemble.forest.RandomForestClassifier 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