Package Exports

ml-random-forest

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Readme

Random forest

Random forest for classification and regression

Installation

$ npm install --save ml-random-forest

Usage

As classifier

import IrisDataset from 'ml-dataset-iris';
import {RandomForestClassifier as RFClassifier} from 'ml-random-forest';

var trainingSet = IrisDataset.getNumbers();
var predictions = IrisDataset.getClasses().map(
    (elem) => IrisDataset.getDistinctClasses().indexOf(elem)
);

var options = {
    seed: 3,
    maxFeatures: 0.8,
    replacement: true,
    nEstimators: 25
};

var classifier = new RFClassifier(options);
classifier.train(trainingSet, predictions);
var result = classifier.predict(trainingSet);

As regression

import {RandomForestRegression as RFRegression} from 'ml-random-forest';

var dataset = [
    [73, 80, 75, 152],
    [93, 88, 93, 185],
    [89, 91, 90, 180],
    [96, 98, 100, 196],
    [73, 66, 70, 142],
    [53, 46, 55, 101],
    [69, 74, 77, 149],
    [47, 56, 60, 115],
    [87, 79, 90, 175],
    [79, 70, 88, 164],
    [69, 70, 73, 141],
    [70, 65, 74, 141],
    [93, 95, 91, 184],
    [79, 80, 73, 152],
    [70, 73, 78, 148],
    [93, 89, 96, 192],
    [78, 75, 68, 147],
    [81, 90, 93, 183],
    [88, 92, 86, 177],
    [78, 83, 77, 159],
    [82, 86, 90, 177],
    [86, 82, 89, 175],
    [78, 83, 85, 175],
    [76, 83, 71, 149],
    [96, 93, 95, 192]
];

var trainingSet = new Array(dataset.length);
var predictions = new Array(dataset.length);

for (var i = 0; i < dataset.length; ++i) {
    trainingSet[i] = dataset[i].slice(0, 3);
    predictions[i] = dataset[i][3];
}

var options = {
    seed: 3,
    maxFeatures: 2,
    replacement: false,
    nEstimators: 200
};

var regression = new RFRegression(options);
regression.train(trainingSet, predictions);
var result = regression.predict(trainingSet);

API Documentation

License

MIT