/* eslint-disable no-console */ /* eslint-disable no-sync */ // 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.