Package Exports
- ml-random-forest
- ml-random-forest/random-forest.js
- ml-random-forest/src/index.js
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Readme
ml-random-forest
Random forest for classification and regression.
Installation
npm i ml-random-forest
API Documentation
Usage
As classifier
import IrisDataset from 'ml-dataset-iris';
import { RandomForestClassifier as RFClassifier } from 'ml-random-forest';
const trainingSet = IrisDataset.getNumbers();
const predictions = IrisDataset.getClasses().map((elem) =>
IrisDataset.getDistinctClasses().indexOf(elem)
);
const options = {
seed: 3,
maxFeatures: 0.8,
replacement: true,
nEstimators: 25
};
const classifier = new RFClassifier(options);
classifier.train(trainingSet, predictions);
const result = classifier.predict(trainingSet);
const oobResult = classifier.predictOOB();
const confusionMatrix = classifier.getConfusionMatrix();As regression
import { RandomForestRegression as RFRegression } from 'ml-random-forest';
const 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]
];
const trainingSet = new Array(dataset.length);
const predictions = new Array(dataset.length);
for (let i = 0; i < dataset.length; ++i) {
trainingSet[i] = dataset[i].slice(0, 3);
predictions[i] = dataset[i][3];
}
const options = {
seed: 3,
maxFeatures: 2,
replacement: false,
nEstimators: 200
};
const regression = new RFRegression(options);
regression.train(trainingSet, predictions);
const result = regression.predict(trainingSet);