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();