---
title: "Preprocessing Utilities in JavaScript with @kanaries/ml"
description: "Prepare numeric and categorical features with the @kanaries/ml Preprocessing JavaScript and TypeScript utilities for browser and Node.js machine learning pipelines."
canonical_url: "https://ml.kanaries.net/docs/apis/utils/preprocessing"
markdown_url: "https://ml.kanaries.net/docs/apis/utils/preprocessing.md"
---
# Preprocessing Utilities in JavaScript

## Algorithm overview

Preprocessing utilities convert raw arrays into model-ready features. They cover scaling, normalization, label and categorical encoding, binarization, missing-value imputation, variance filtering, and univariate feature selection.

## JavaScript implementation

`@kanaries/ml` exports these helpers under `utils.Preprocessing`, so feature preparation can run in the same JavaScript or TypeScript runtime as model training and prediction.

## Quick start example

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

const scaler = new utils.Preprocessing.StandardScaler();
const scaled = scaler.fitTransform([[1, 10], [2, 20], [3, 30]]);

const encoder = new utils.Preprocessing.OneHotEncoder({ drop: 'ifBinary' });
const encoded = encoder.fitTransform([
  ['red', 'S'],
  ['blue', 'M'],
]);
console.log(encoded);
```

## Detailed API reference

### Scalers and normalizers

```ts
new utils.Preprocessing.StandardScaler({ withMean?: boolean; withStd?: boolean })
new utils.Preprocessing.MinMaxScaler({ featureRange?: [number, number] })
new utils.Preprocessing.MaxAbsScaler()
new utils.Preprocessing.Normalizer({ norm?: 'l1' | 'l2' | 'max' })
```

Scaler methods:

- `fit(X: number[][]): void`
- `transform(X: number[][]): number[][]`
- `fitTransform(X: number[][]): number[][]`
- `inverseTransform(X: number[][]): number[][]` for `StandardScaler`, `MinMaxScaler`, and `MaxAbsScaler`

`Normalizer` supports `fit`, `transform`, and `fitTransform`; `fit` is a no-op.

### Encoders and binarizer

```ts
new utils.Preprocessing.LabelEncoder()
new utils.Preprocessing.OrdinalEncoder()
new utils.Preprocessing.OneHotEncoder({ drop?: 'none' | 'first' | 'ifBinary' })
new utils.Preprocessing.Binarizer({ threshold?: number })
```

`LabelEncoder` works on numeric label arrays. `OrdinalEncoder` and `OneHotEncoder` accept categorical matrices containing `string`, `number`, `boolean`, or `null` values.

### Imputation and feature selection

```ts
new utils.Preprocessing.SimpleImputer({
  strategy?: 'mean' | 'median' | 'mostFrequent' | 'constant';
  fillValue?: number;
  missingValues?: number | null;
})

new utils.Preprocessing.VarianceThreshold({ threshold?: number })
utils.Preprocessing.fRegression(X: number[][], y: number[]): number[]
new utils.Preprocessing.SelectKBest({ k?: number; scoreFunc?: FeatureScoreFunc })
```

`SimpleImputer` supports `fit`, `transform`, and `fitTransform`. `VarianceThreshold` and `SelectKBest` remove features during `transform`.

## JavaScript deployment notes

- Fit preprocessing on training data only, then reuse the fitted transformer for inference.
- Keep encoder category assumptions stable between browser sessions or Node.js jobs.
- Use `fitTransform` for quick experiments, but keep explicit `fit` and `transform` calls in production pipelines.