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decision-tree

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Readme

Decision Tree for NodeJS

This module contains the NodeJS Implementation of Decision Tree using ID3 Algorithm

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();
```