---
title: "Self-Training Classifier in JavaScript and TypeScript"
description: "Pseudo-label confident unlabeled samples with the @kanaries/ml SelfTrainingClassifier JavaScript implementation in browser or Node.js."
canonical_url: "https://ml.kanaries.net/docs/apis/semi_supervised/selfTrainingClassifier"
markdown_url: "https://ml.kanaries.net/docs/apis/semi_supervised/selfTrainingClassifier.md"
---
# Self-Training Classifier in JavaScript

## Algorithm overview

Self-training repeatedly fits a probabilistic classifier, assigns pseudo-labels to the most confident unlabeled rows, and refits with the expanded labeled set. It helps when labels are scarce but class probabilities are reasonably calibrated.

## JavaScript implementation

`@kanaries/ml` wraps any registered classifier with `predictProba`, including `GaussianNB`. Use `-1` for unlabeled targets and choose a confidence threshold or a fixed number of additions per iteration.

## Quick start example

```ts
import { Bayes, SemiSupervised } from '@kanaries/ml';

const model = new SemiSupervised.SelfTrainingClassifier({
  estimator: new Bayes.GaussianNB(), threshold: 0.8, maxIter: 10,
});
model.fit([[-3], [-2], [-1], [1], [2], [3]], [0, 0, -1, -1, 1, 1]);
console.log(model.transduction);
```

## Detailed API reference

Options: `estimator`, `threshold` (default `.75`), `criterion: 'threshold' | 'kBest'`, `kBest` (default `10`), and `maxIter` (default `10`).

Methods: `fit`, `predict`, `predictProba`. Learned fields: `transduction`, `labeledIteration`, `nIter`, and `terminationCondition`. Seed labels retain iteration `0`; labels that remain unknown retain `-1`.