DecisionTree

NodeJS Implementation of Decision Tree using ID3 Algorithm. Base decision tree based on this github repo.

Features:

• Simple APIs
• Debug mode
• Inbuilt persist model capability
• Highly configurable
• Trained model can be imported/exported easily

Installation

Dillinger requires Node.js v6+ to run.

$ npm install js-decisiontree --save

How to use

const { Tree } = require('js-decisiontree');
const trainingDataSet = [];
const config = {};
const DecisionTree = new Tree(config);
DecisionTree.train(trainingDataSet, config);
DecisionTree.predict(sample);

Configuration settings

Setting	Description
className	Class name or property which will be used as output of decision tree
features	Features or data points to be used for training decision tree
persist	If set, persists the trained model on local disk
learn	If set, trains the model with data used for prediction
fixMissingFeatures	If set, takes careof of missing features in training data
debug	If set, logs the internal activity to terminal
load	If set, loads previously stored model to local disk. This setting is only significant while intializing the tree

APIs

Setting	Description
train	Training the decision tree
predict	Prediciting the results
toJSON	Export the trained model as JSON
fromJSON	Import an already trained JSON model exported using .toJSON() API

Examples

Refer examples for exhautive examples

const DecisionTree = new Tree();
const trainingDataSet = [
    {"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}
];
const config = {
    className: 'liked',
    features: [ 'color', 'shape' ],
};
const sample = {"color":"blue", "shape":"hexagon", "liked":false }; 
DecisionTree.train(trainingDataSet, config);
const prediction = DecisionTree.predict(sample);
console.log("prediction:", prediction); // false

License

MIT

Free Software, Hell Yeah!