---
title: "Spline, Target, and Multi-Label Preprocessing in JavaScript"
description: "Build spline bases, leakage-safe target encodings, and multi-label indicator matrices in JavaScript or TypeScript with @kanaries/ml."
canonical_url: "https://ml.kanaries.net/docs/apis/utils/advancedPreprocessing"
markdown_url: "https://ml.kanaries.net/docs/apis/utils/advancedPreprocessing.md"
---
# Advanced Preprocessing in JavaScript

## Algorithm overview

Spline features capture smooth nonlinear relationships, target encoding converts high-cardinality categories into compact numeric features, and multi-label binarization turns sets of labels into indicator matrices. These transformations are useful when ordinary scaling or one-hot encoding does not fit the data shape.

## JavaScript implementation

`@kanaries/ml` runs all three transformations locally in browser or Node.js. `SplineTransformer` creates a B-spline basis per numeric feature. `TargetEncoder.fitTransform` uses cross-fitting to keep a row's own target out of its encoding. `MultiLabelBinarizer` can return a dense matrix or the library's serializable CSR representation.

## Quick start

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

const spline = new utils.SplineTransformer({ nKnots: 4, degree: 3 });
const curved = spline.fitTransform([[0], [1], [2], [3]]);

const encoder = new utils.TargetEncoder({ smooth: 2, cv: 3 });
const encoded = encoder.fitTransform(
  [['free'], ['pro'], ['free'], ['team'], ['pro'], ['team']],
  [2, 12, 4, 20, 10, 22],
);

const labels = new utils.MultiLabelBinarizer();
const indicators = labels.fitTransform([['search', 'ml'], ['charts'], []]);
console.log({ curved, encoded, indicators });
```

## Detailed API reference

`SplineTransformer` accepts `nKnots`, `degree`, `knots` (`uniform`, `quantile`, or explicit per-feature knots), `extrapolation`, and `includeBias`.

`TargetEncoder` accepts `categories`, `targetType` (`auto`, `continuous`, or `binary`), `smooth`, `cv`, `shuffle`, and `randomState`. Unknown categories map to the global target mean.

`MultiLabelBinarizer` accepts optional ordered `classes` and `sparseOutput`. It exposes `fit`, `transform`, `fitTransform`, `inverseTransform`, and fitted `classes`.