ml-random-forest

Random forest for classification and regression.

Installation

npm i ml-random-forest

API Documentation

Usage

As classifier

importIrisDatasetfrom'ml-dataset-iris';import{RandomForestClassifierasRFClassifier}from'ml-random-forest';consttrainingSet=IrisDataset.getNumbers();constpredictions=IrisDataset.getClasses().map((elem)=>IrisDataset.getDistinctClasses().indexOf(elem));constoptions={seed: 3,maxFeatures: 0.8,replacement: true,nEstimators: 25};constclassifier=newRFClassifier(options);classifier.train(trainingSet,predictions);constresult=classifier.predict(trainingSet);constoobResult=classifier.predictOOB();constconfusionMatrix=classifier.getConfusionMatrix();

As regression

import{RandomForestRegressionasRFRegression}from'ml-random-forest';constdataset=[[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]];consttrainingSet=newArray(dataset.length);constpredictions=newArray(dataset.length);for(leti=0;i<dataset.length;++i){trainingSet[i]=dataset[i].slice(0,3);predictions[i]=dataset[i][3];}constoptions={seed: 3,maxFeatures: 2,replacement: false,nEstimators: 200};constregression=newRFRegression(options);regression.train(trainingSet,predictions);constresult=regression.predict(trainingSet);

License

MIT