---
title: "Transformed Target Regression in JavaScript and TypeScript"
description: "Train regressors on log-scaled or otherwise transformed targets in browser and Node.js applications with @kanaries/ml TransformedTargetRegressor."
canonical_url: "https://ml.kanaries.net/docs/apis/compose"
markdown_url: "https://ml.kanaries.net/docs/apis/compose/index.html.md"
---
# Transformed Target Regression in JavaScript

## Algorithm overview

Regression targets are often skewed, strictly positive, or easier to model after a monotonic transformation. `TransformedTargetRegressor` applies that transformation during training and automatically maps predictions back to the original units.

## JavaScript implementation

`@kanaries/ml` composes any serializable regressor with a transformer that implements `fit`, `transform`, and `inverseTransform`. Both components are cloned before fitting, work in browser or Node.js, and survive model serialization.

## Interactive transformed-target regression playground

Compare identity and log-transformed targets on positive data, then add observations directly to the chart. `Compose.TransformedTargetRegressor` handles transformation, fitting, and inverse prediction live.

> The HTML version includes an interactive Transformed-Target 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/compose).

## Quick start

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

const model = new Compose.TransformedTargetRegressor({
  regressor: new Linear.LinearRegression(),
  transformer: new utils.Preprocessing.FunctionTransformer({
    func: 'log1p',
    inverseFunc: 'expm1',
  }),
});

model.fit([[0], [1], [2]], [1, 3, 7]);
model.predict([[3]]); // approximately [15]
```

## Detailed API reference

`new TransformedTargetRegressor({ regressor?, transformer?, func?, inverseFunc? })` defaults to linear regression and an identity transform. Pass either a transformer or a `func`/`inverseFunc` pair; the two forms are mutually exclusive. `fit(X, y)` trains cloned components, `predict(X)` returns inverse-transformed values, and `score(X, y)` reports R² in the original target space. Nested parameters use `regressor__param` and `transformer__param` in `setParams`.