NodeJS Implementation of Decision Tree using ID3 Algorithm.
npm install js-decisiontree
Features:
- Simple APIs
- Debug mode
- Inbuilt persist model capability
- Highly configurable
- Trained model can be imported/exported easily
Dillinger requires Node.js v6+ to run.
``sh`
$ npm install js-decisiontree --save
js
const { Tree } = require('js-decisiontree');
const trainingDataSet = [];
const config = {};
const DecisionTree = new Tree(config);
DecisionTree.train(trainingDataSet, config);
DecisionTree.predict(sample);
`
$3
| 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 |
$3
| 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
js
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!