---
title: "Permutation Importance and Partial Dependence in JavaScript"
description: "Explain JavaScript and TypeScript machine-learning models with permutation importance and partial dependence in browser or Node.js using @kanaries/ml."
canonical_url: "https://ml.kanaries.net/docs/apis/utils/inspection"
markdown_url: "https://ml.kanaries.net/docs/apis/utils/inspection.md"
---
# Model Inspection in JavaScript

## Algorithm overview

Permutation importance measures how much a fitted model's score degrades when one feature is shuffled. Partial dependence averages predictions while forcing one or two features across a grid. Together they help identify influential inputs and visualize broad model response patterns.

## JavaScript implementation

`@kanaries/ml` exposes model-agnostic inspection functions that work with fitted estimators in browser and Node.js. They do not depend on tree internals or linear coefficients, so the same workflow works across compatible models.

## Quick start

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

const X = [[0, 0], [1, 0], [2, 1], [3, 1]];
const y = [0, 2, 4, 6];
const model = new Linear.LinearRegression();
model.fit(X, y);

const importance = utils.permutationImportance(model, X, y, {
  nRepeats: 10,
  randomState: 42,
});
const dependence = utils.partialDependence(model, X, [0], {
  gridResolution: 50,
});
console.log({ importance, dependence });
```

## Detailed API reference

`permutationImportance(estimator, X, y, { nRepeats?, randomState?, scoring? })` returns `importances`, `importancesMean`, and `importancesStd` by feature.

`partialDependence(estimator, X, features, { gridResolution?, percentiles? })` supports one or two numeric feature indices and returns `gridValues` plus averaged predictions shaped by the requested grid.