:: Experimental :: Decision tree model for classification or regression.
:: Experimental :: Represents a gradient boosted trees model.
:: Experimental :: Represents a gradient boosted trees model.
:: DeveloperApi :: Information gain statistics for each split
:: DeveloperApi :: Information gain statistics for each split
:: DeveloperApi :: Node in a decision tree.
:: DeveloperApi :: Node in a decision tree.
About node indexing: Nodes are indexed from 1. Node 1 is the root; nodes 2, 3 are the left, right children. Node index 0 is not used.
Predicted value for a node
Predicted value for a node
:: Experimental :: Represents a random forest model.
:: Experimental :: Represents a random forest model.
:: DeveloperApi :: Split applied to a feature
:: DeveloperApi :: Split applied to a feature
feature index
Threshold for continuous feature. Split left if feature <= threshold, else right.
type of feature -- categorical or continuous
Split left if categorical feature value is in this set, else right.
:: Experimental :: Decision tree model for classification or regression. This model stores the decision tree structure and parameters.