---
title: "IterativeImputer in JavaScript and TypeScript"
description: "Estimate each missing numeric feature from the others using the @kanaries/ml JavaScript IterativeImputer implementation in browser or Node.js."
canonical_url: "https://ml.kanaries.net/docs/apis/impute/iterativeImputer"
markdown_url: "https://ml.kanaries.net/docs/apis/impute/iterativeImputer.md"
---
# IterativeImputer in JavaScript

## Algorithm overview

Iterative imputation begins with a column statistic, then repeatedly predicts each feature from the other features. It is useful when missing columns are correlated and a single mean or median would erase that relationship.

## JavaScript implementation

`@kanaries/ml` clones and fits a registered regressor for every imputation step, records the fitted sequence for later `transform`, and supports deterministic ordering and bounds. Until `BayesianRidge` is added, the default base estimator is lightly regularized `RidgeRegression`; supply another registered regressor when needed.

## Quick start example

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

const imputer = new Impute.IterativeImputer({ maxIter: 10, tol: 1e-3 });
const complete = imputer.fitTransform([[0, 1], [1, 3], [2, NaN], [3, 7]]);
const future = imputer.transform([[4, NaN]]);
console.log({ complete, future });
```

## Detailed API reference

Options: `estimator`, `maxIter`, `tol`, `initialStrategy: 'mean' | 'median' | 'mostFrequent' | 'constant'`, `fillValue`, `imputationOrder: 'ascending' | 'descending' | 'roman' | 'arabic' | 'random'`, `skipComplete`, `minValue`, `maxValue`, and `randomState`.

Methods: `fit`, `fitTransform`, and `transform`. Learned `imputationSequence` and `nIter` are exposed. Missing values must be represented by `NaN`.