https://github.com/serendipious/nodejs-decision-tree

NodeJS Implementation of Decision Tree using ID3 Algorithm
https://github.com/serendipious/nodejs-decision-tree

Last synced: 4 months ago
JSON representation

NodeJS Implementation of Decision Tree using ID3 Algorithm

• Host: GitHub
• URL: https://github.com/serendipious/nodejs-decision-tree
• Owner: serendipious
• License: mit
• Created: 2013-04-28T11:11:36.000Z (over 11 years ago)
• Default Branch: master
• Last Pushed: 2022-10-22T17:11:06.000Z (about 2 years ago)
• Last Synced: 2024-04-13T21:02:34.291Z (8 months ago)
• Language: JavaScript
• Homepage: https://npmjs.org/package/decision-tree
• Size: 88.9 KB
• Stars: 210
• Watchers: 7
• Forks: 81
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • Changelog: HISTORY.md
  • License: LICENSE

Awesome Lists containing this project

README

        
Decision Tree for Node.js

========================

This Node.js module implements a Decision Tree using the [ID3 Algorithm](http://en.wikipedia.org/wiki/ID3_algorithm)

# [Installation](id:installation)

    npm install decision-tree

# [Usage](id:usage)

## Import the module

```js

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

```

## Prepare training dataset

```js

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

```js

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

```js

var class_name = "liked";

```

## Setup Features to be used by decision tree

```js

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

```

## Create decision tree and train the model

```js

var dt = new DecisionTree(class_name, features);

dt.train(training_data);

```

Alternately, you can also create and train the tree when instantiating the tree itself:

```js

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

```

## Predict class label for an instance

```js

var predicted_class = dt.predict({

  color: "blue",

  shape: "hexagon"

});

```

## Evaluate model on a dataset

```js

var accuracy = dt.evaluate(test_data);

```

## Export underlying model for visualization or inspection

```js

var treeJson = dt.toJSON();

```

## Create a decision tree from a previously trained model

```js

var treeJson = dt.toJSON();

var preTrainedDecisionTree = new DecisionTree(treeJson);

```

Alternately, you can also import a previously trained model on an existing tree instance, assuming the features & class are the same:

```js

var treeJson = dt.toJSON();

dt.import(treeJson);

```