Package Exports

ml-random-forest

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Readme

ml-random-forest

Random forest for classification and regression.

Installation

npm install --save ml-random-forest

API Documentation

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);

License

MIT