InferID3Tree builds a decision tree using a RuleGenerator from a set of Instances (implements the ID3 algorithm).

Function to Create New Decision Tree Classifier.

Interface for creating new Decision Tree Regressor.

NewID3DecisionTree returns a new ID3DecisionTree with the specified test-prune ratio and InformationGain as the rule generator.

NewID3DecisionTreeFromRule returns a new ID3DecisionTree with the specified test-prun ratio and the given rule gnereator.

Function to create a new isolation forest.

NewRandomTree returns a new RandomTree which considers attrs randomly chosen attributes at each node.

LeafNode means there are no children.

RuleNode means we should look at the next attribute value.

CARTDecisionTreeClassifier: Tree struct for Decision Tree Classifier It contains the rootNode, as well as all of the hyperparameters chosen by the user.

CARTDecisionTreeRegressor - Tree struct for Decision Tree Regressor It contains the rootNode, as well as the hyperparameters chosen by user.

DecisionTreeNode represents a given portion of a decision tree.

DecisionTreeRule represents the "decision" in "decision tree".

GiniCoefficientRuleGenerator generates DecisionTreeRules which minimize the Geni impurity coefficient at each node.

ID3DecisionTree represents an ID3-based decision tree using the Information Gain metric to select which attributes to split on at each node.

InformationGainRatioRuleGenerator generates DecisionTreeRules which maximise the InformationGain at each node.

InformationGainRuleGenerator generates DecisionTreeRules which maximize information gain at each node.

RandomTree builds a decision tree by considering a fixed number of randomly-chosen attributes at each node.

RandomTreeRuleGenerator is used to generate decision rules for Random Trees.

RuleGenerator implementations analyse instances and determine the best value to split on.

NodeType determines whether a DecisionTreeNode is a leaf or not.