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.
Use npm to install:
npm install wink-regression-tree --save
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. A sample data row is given for quick reference:
| Model | MPG | Cylinders | Displacement | Power | Weight | Acceleration | Year | Origin |
|---|---|---|---|---|---|---|---|---|
| Toyota Mark II | 20 | 6 | large displacement | high power | high weight | slow | 73 | Japan |
The code below provides a potential configuration to predict the value of miles per gallon:
// 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.
For detailed API docs, check out http://winkjs.org/wink-regression-tree/ URL!
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.
Wink is a family of open source packages for Statistical Analysis, Natural Language Processing and Machine Learning in NodeJS. The code is thoroughly documented for easy human comprehension and has a test coverage of ~100% for reliability to build production grade solutions.
wink-regression-tree is copyright 2017-18 GRAYPE Systems Private Limited.
It is licensed under the terms of the MIT License.