@__username/decision-tree v0.4.0

Decision Tree for NodeJS

This module contains the NodeJS Implementation of Decision Tree using ID3 Algorithm

Table Of Contents

• Installation
• Usage Import Module Prepare * Installation

Installation

npm install decision-tree

Usage

• Import the module:

  		var DecisionTree = require('decision-tree');

• Prepare training dataset:

  		var training_data = [
  			{"color":"blue", "shape":"square", "liked":false},
  			{"color":"red", "shape":"square", "liked":false},
  			{"color":"blue", "shape":"circle", "liked":true},
  			{"color":"red", "shape":"circle", "liked":true},
  			{"color":"blue", "shape":"hexagon", "liked":false},
  			{"color":"red", "shape":"hexagon", "liked":false},
  			{"color":"yellow", "shape":"hexagon", "liked":true},
  			{"color":"yellow", "shape":"circle", "liked":true}
  		];

• Prepare test dataset:

  		var test_data = [
  			{"color":"blue", "shape":"hexagon", "liked":false},
  			{"color":"red", "shape":"hexagon", "liked":false},
  			{"color":"yellow", "shape":"hexagon", "liked":true},
  			{"color":"yellow", "shape":"circle", "liked":true}
  		];

• Setup Target Class used for prediction:

  		var class_name = "liked";

• Setup Features to be used by decision tree:

  		var features = ["color", "shape"];

• Create decision tree and train model:

  		var dt = new DecisionTree(training_data, class_name, features);

• Predict class label for an instance:

  		var predicted_class = dt.predict({
  			color: "blue",
  			shape: "hexagon"
  		});

• Evaluate model on a dataset:

  		var accuracy = dt.evaluate(test_data);

• Export underlying model for visualization or inspection:

  		var treeModel = dt.toJSON();