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ID3 Decision Tree

The ID3 Decision Tree is an implementation of the ID3 algorithm for constructing decision trees in JavaScript. It builds a decision tree based on a given dataset and target attribute.

Classes and Functions

Class: DecisionTreeNode

Represents a node in the decision tree.

Constructor

Creates a new instance of the DecisionTreeNode class.

new DecisionTreeNode(attribute, value, branches, classification)
  • attribute (string): The attribute associated with the node.
  • value (string): The attribute value associated with the node.
  • branches (Array<[string, DecisionTreeNode]>): The branches of the node, where each branch is a value-node pair.
  • classification (string): The classification of the node if it is a leaf node.

Class: DecisionTree

Represents the decision tree.

Constructor

Creates a new instance of the DecisionTree class.

new DecisionTree(dataset, targetAttribute)
  • dataset (Array<object>): The dataset used to build the tree.
  • targetAttribute (string): The attribute to predict.

Methods

.buildTree(examples, attributes, targetAttribute)

Builds the decision tree recursively using the ID3 algorithm.

  • examples (Array<object>): The dataset for the current node.
  • attributes (string[]): The attributes available for splitting.
  • targetAttribute (string): The target attribute to predict.
  • Returns: A DecisionTreeNode representing the root of the decision tree.

.predict(example)

Predicts the classification of an example using the decision tree.

  • example (object): The example to predict.
  • Returns: The predicted classification.

Function: id3

Constructs a decision tree using the ID3 algorithm.

  • dataset (Array<object>): The dataset to build the tree from.
  • targetAttribute (string): The attribute to predict.
  • Returns: A DecisionTree instance.

Function: entropy

Calculates the entropy of a dataset. Entropy is a measure of the amount of uncertainty in a dataset. The higher the entropy, the more uncertain the dataset is.

  • examples (Array): The dataset to calculate entropy for.
  • targetAttribute (string): The attribute to predict.
  • Returns: The entropy value.

    • Function: predict

    Predicts the classification using a decision tree.

    • example (object): The example to predict.
    • tree (DecisionTreeNode): The decision tree to use.
    • Returns: The predicted classification.