@__username/decision-tree

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();