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Installation\n\n`npm i ml-random-forest`\n\n## [API Documentation](https://mljs.github.io/random-forest/)\n\n## Usage\n\n### As classifier\n\n```js\nimport IrisDataset from 'ml-dataset-iris';\nimport { RandomForestClassifier as RFClassifier } from 'ml-random-forest';\n\nconst trainingSet = IrisDataset.getNumbers();\nconst predictions = IrisDataset.getClasses().map((elem) =>\n  IrisDataset.getDistinctClasses().indexOf(elem)\n);\n\nconst options = {\n  seed: 3,\n  maxFeatures: 0.8,\n  replacement: true,\n  nEstimators: 25\n};\n\nconst classifier = new RFClassifier(options);\nclassifier.train(trainingSet, predictions);\nconst result = classifier.predict(trainingSet);\nconst oobResult = classifier.predictOOB();\nconst confusionMatrix = classifier.getConfusionMatrix();\n```\n\n### As regression\n\n```js\nimport { RandomForestRegression as RFRegression } from 'ml-random-forest';\n\nconst dataset = [\n  [73, 80, 75, 152],\n  [93, 88, 93, 185],\n  [89, 91, 90, 180],\n  [96, 98, 100, 196],\n  [73, 66, 70, 142],\n  [53, 46, 55, 101],\n  [69, 74, 77, 149],\n  [47, 56, 60, 115],\n  [87, 79, 90, 175],\n  [79, 70, 88, 164],\n  [69, 70, 73, 141],\n  [70, 65, 74, 141],\n  [93, 95, 91, 184],\n  [79, 80, 73, 152],\n  [70, 73, 78, 148],\n  [93, 89, 96, 192],\n  [78, 75, 68, 147],\n  [81, 90, 93, 183],\n  [88, 92, 86, 177],\n  [78, 83, 77, 159],\n  [82, 86, 90, 177],\n  [86, 82, 89, 175],\n  [78, 83, 85, 175],\n  [76, 83, 71, 149],\n  [96, 93, 95, 192]\n];\n\nconst trainingSet = new Array(dataset.length);\nconst predictions = new Array(dataset.length);\n\nfor (let i = 0; i < dataset.length; ++i) {\n  trainingSet[i] = dataset[i].slice(0, 3);\n  predictions[i] = dataset[i][3];\n}\n\nconst options = {\n  seed: 3,\n  maxFeatures: 2,\n  replacement: false,\n  nEstimators: 200\n};\n\nconst regression = new RFRegression(options);\nregression.train(trainingSet, predictions);\nconst result = regression.predict(trainingSet);\n```\n\n## License\n\n[MIT](./LICENSE)\n\n[npm-image]: 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