RandomForestClassifierModel train

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train(self, frame, label_column, observation_columns, num_classes=2, num_trees=1, impurity='gini', max_depth=4, max_bins=100, seed=-1236386969, categorical_features_info=None, feature_subset_category=None)

[ALPHA] Build Random Forests Classifier model.

Parameters:

frame : Frame

A frame to train the model on

label_column : unicode

Column name containing the label for each observation

observation_columns : list

Column(s) containing the observations

num_classes : int32 (default=2)

Number of classes for classification. Default is 2.

num_trees : int32 (default=1)

Number of tress in the random forest. Default is 1.

impurity : unicode (default=gini)

Criterion used for information gain calculation. Supported values “gini” or “entropy”. Default is “gini”.

max_depth : int32 (default=4)

Maximum depth of the tree. Default is 4.

max_bins : int32 (default=100)

Maximum number of bins used for splitting features. Default is 100.

seed : int32 (default=-1236386969)

Random seed for bootstrapping and choosing feature subsets. Default is a randomly chosen seed.

categorical_features_info : dict (default=None)

Arity of categorical features. Entry (n-> k) indicates that feature ‘n’ is categorical with ‘k’ categories indexed from 0:{0,1,...,k-1}.

feature_subset_category : unicode (default=None)

Number of features to consider for splits at each node. Supported values “auto”,”all”,”sqrt”,”log2”,”onethird”. If “auto” is set, this is based on num_trees: if num_trees == 1, set to “all” ; if num_trees > 1, set to “sqrt”

Returns:

: dict

dictionary

A dictionary with trained Random Forest Classifier model with the following keys: |‘observation_columns’: the list of observation columns on which the model was trained, |‘label_column’: the column name containing the labels of the observations, |‘num_classes’: the number of classes, |‘num_trees’: the number of decision trees in the random forest, |‘num_nodes’: the number of nodes in the random forest, |‘feature_subset_category’: the map storing arity of categorical features, |‘impurity’: the criterion used for information gain calculation, |‘max_depth’: the maximum depth of the tree, |‘max_bins’: the maximum number of bins used for splitting features, |‘seed’: the random seed used for bootstrapping and choosing feature subset.

Creating a Random Forests Classifier Model using the observation columns and label column.

Examples

See here for examples.