wink-regression-tree

Decision Tree to predict the value of a continuous target variable

Predict the value of a continuous variable such as price, turn around time, or mileage using wink-regression-tree. It is a part of wink — a growing family of high quality packages for Statistical Analysis, Natural Language Processing and Machine Learning in NodeJS.

Installation

Use npm to install:

npm install wink-regression-tree --save

Getting Started

Here is an example of predicting car’s mileage (miles per gallon - mpg) from attributes like displacement, horsepower, acceleration, country of origin, and few more. The data set used in this example has been quantized for a quick start.

// Load wink-regression-tree.
var regressionTree = require( 'wink-regression-tree' );

// Load cars training data set.
// In practice an async mechanism may be used to
// read data asynchronously and call `ingest()` on
// every row of data read.
var cars = require( 'wink-regression-tree/sample-data/cars.json' );

// Create a sample data to test prediction for
// Ford Gran Torino, having "mpg of 14.5", very
// large displacement, extremely high power, very
// high weight, slow, and with origin as US.
var input = {
  model: 'Ford Gran Torino',
  weight: 'very high weight',
  displacement: 'very large displacement',
  horsepower: 'extremely high power',
  origin: 'US',
  acceleration: 'slow'
};
// Above record is not the part of training data.

// Create an instance of the regression  tree.
var rt = regressionTree();

// Specify columns of the training data.
var columns = [
  { name: 'model', categorical: true, exclude: true },
  { name: 'mpg', categorical: false, target: true },
  { name: 'cylinders', categorical: true, exclude: false },
  { name: 'displacement', categorical: true, exclude: false },
  { name: 'horsepower', categorical: true, exclude: false },
  { name: 'weight', categorical: true, exclude: false },
  { name: 'acceleration', categorical: true, exclude: false },
  { name: 'year', categorical: true, exclude: true },
  { name: 'origin', categorical: true, exclude: false  }
];
// Specify configuration for learning.
var treeParams = {
  minPercentVarianceReduction: 0.5,
  minLeafNodeItems: 10,
  minSplitCandidateItems: 30,
  minAvgChildrenItems: 2
};
// Define the regression tree configuration using
// `columns` and `treeParams`.
rt.defineConfig( columns, treeParams );

// Ingest the data.
cars.forEach( function ( row ) {
  rt.ingest( row );
} );

// Data ingested! Now time to learn from data!!
console.log( rt.learn() );
// -> 16 (Number of Rules Learned)

// Predict the **mean** value.
var mean = rt.predict( input );
console.log( +mean.toFixed( 1 ) );
// -> 14.3 ( compare with actual mpg of 14.5 )

// In practice one may like to compute a range
// or upper limit using the `modifier` function
// during prediction. Note `size`, `mean`, and `stdev`
// values, passed to this function, can be used
// for computing the range or the upper limit.

Try experimenting with this example on Runkit in the browser.

Documentation

For detailed API docs, check out https://winkjs.github.io/wink-regression-tree/ URL!

Need Help?

If you spot a bug and the same has not yet been reported, raise a new issue or consider fixing it and sending a pull request.

Copyright & License

wink-regression-tree is copyright 2017 GRAYPE Systems Private Limited.

It is licensed under the under the terms of the GNU Affero General Public License as published by the Free Software Foundation, version 3 of the License.