---
title: "Robust Regression in JavaScript and TypeScript"
description: "Fit Huber, RANSAC, Theil-Sen, and quantile regression models in browser or Node.js applications with @kanaries/ml."
canonical_url: "https://ml.kanaries.net/docs/apis/linear/robustRegressors"
markdown_url: "https://ml.kanaries.net/docs/apis/linear/robustRegressors.md"
---
# Robust regression in JavaScript

Ordinary least squares can move sharply when targets contain outliers. `HuberRegressor` softens large residuals, `RANSACRegressor` fits a consensus set, `TheilSenRegressor` aggregates many subsample fits, and `QuantileRegressor` estimates a conditional quantile instead of a mean.

## JavaScript implementation

`@kanaries/ml` exposes sklearn-style constructors and `fit`/`predict` methods that run in browser or Node.js. Use Huber for moderate contamination, RANSAC for clear gross outliers, Theil-Sen for small robust datasets, and quantile regression for medians or prediction bands.

## Interactive robust regression playground

Switch between Huber, RANSAC, Theil–Sen, and quantile regression, then try the outlier dataset or add extreme points yourself. Each curve is trained live by the selected `@kanaries/ml` estimator.

> The HTML version includes an interactive Robust Regression playground powered by @kanaries/ml. You can change the dataset, noise, and model controls; add observations; and inspect live predictions plus train and holdout metrics. The runnable guide and API reference continue below. [Open the HTML page](https://ml.kanaries.net/docs/apis/linear/robustRegressors).

## Quick start

```ts
import { Linear } from '@kanaries/ml';

const model = new Linear.RANSACRegressor({ randomState: 42 });
model.fit([[0], [1], [2], [3]], [0, 1, 20, 3]);
console.log(model.predict([[4]]));
```

## Detailed API reference

- `HuberRegressor({ epsilon=1.35, alpha=0.0001, maxIter=100, tol=1e-5, fitIntercept=true })` exposes `coef`, `intercept`, `scale`, and `outliers`.
- `RANSACRegressor({ estimator, minSamples, residualThreshold, maxTrials=100, stopProbability=.99, randomState })` exposes `estimatorFitted`, `inlierMask`, and `nTrials`.
- `TheilSenRegressor({ nSubsamples, maxSubpopulation=10000, maxIter=300, tol=1e-3, randomState })` exposes `coef`, `intercept`, and `nIter`.
- `QuantileRegressor({ quantile=.5, alpha=1, fitIntercept=true, maxIter=5000, tol=1e-7 })` solves the pinball-loss problem with an ADMM optimizer.