Decision Tree for Node.js
This Node.js module implements a Decision Tree using the 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 the model
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:
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 treeJson = dt.toJSON();
Create a decision tree from a previously trained model
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:
var treeJson = dt.toJSON();
dt.import(treeJson);
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