https://github.com/mljs/cross-validation
Utility library to make cross validation with supervised classifiers
https://github.com/mljs/cross-validation
classifier cross-validation supervised-learning
Last synced: 2 months ago
JSON representation
Utility library to make cross validation with supervised classifiers
- Host: GitHub
- URL: https://github.com/mljs/cross-validation
- Owner: mljs
- License: mit
- Created: 2016-07-14T13:10:47.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2020-01-31T13:55:05.000Z (over 5 years ago)
- Last Synced: 2025-04-18T05:20:41.856Z (2 months ago)
- Topics: classifier, cross-validation, supervised-learning
- Language: JavaScript
- Homepage:
- Size: 1.26 MB
- Stars: 8
- Watchers: 5
- Forks: 4
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: History.md
- License: LICENSE
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README
# cross-validation
[![NPM version][npm-image]][npm-url]
[![build status][travis-image]][travis-url]
[![npm download][download-image]][download-url]
Utility library to do cross validation with supervised classifiers.
Cross-validation methods:
- [k-fold](https://en.wikipedia.org/wiki/Cross-validation_(statistics)#k-fold_cross-validation)
- [leave-p-out](https://en.wikipedia.org/wiki/Cross-validation_(statistics)#Leave-p-out_cross-validation)
[API documentation](https://mljs.github.io/cross-validation/).
A list of the mljs supervised classifiers is available [here](https://github.com/mljs/ml#tools) in the supervised learning section, but you could also use your own. Cross validations methods return a ConfusionMatrix ([https://github.com/mljs/confusion-matrix](https://github.com/mljs/confusion-matrix)) that can be used to calculate metrics on your classification result.
## Installation
```bash
npm i -s ml-cross-validation
```
## Example using a ml classification library
```js
const crossValidation = require('ml-cross-validation');
const KNN = require('ml-knn');
const dataset = [[0, 0, 0], [0, 1, 1], [1, 1, 0], [2, 2, 2], [1, 2, 2], [2, 1, 2]];
const labels = [0, 0, 0, 1, 1, 1];
const confusionMatrix = crossValidation.leaveOneOut(KNN, dataSet, labels);
const accuracy = confusionMatrix.getAccuracy();
```
## Example using a classifier with its own specific API
If you have a library that does not comply with the ML Classifier conventions, you can use can use a callback to perform the classification.
The callback will take the train features and labels, and the test features. The callback shoud return the array of predicted labels.
```js
const crossValidation = require('ml-cross-validation');
const KNN = require('ml-knn');
const dataset = [[0, 0, 0], [0, 1, 1], [1, 1, 0], [2, 2, 2], [1, 2, 2], [2, 1, 2]];
const labels = [0, 0, 0, 1, 1, 1];
const confusionMatrix = crossValidation.leaveOneOut(dataSet, labels, function(trainFeatures, trainLabels, testFeatures) {
const knn = new KNN(trainFeatures, trainLabels);
return knn.predict(testFeatures);
});
const accuracy = confusionMatrix.getAccuracy();
```
## ML classifier API conventions
You can write your classification library so that it can be used with ml-cross-validation as described in [here](#example-using-a-ml-classification-library)
For that, your classification library must implement
- A constructor. The constructor can be passed options as a single argument.
- A `train` method. The `train` method is passed the data as a first argument and the labels as a second.
- A `predict` method. The `predict` method is passed test data and should return a predicted label.
### Example
```js
class MyClassifier {
constructor(options) {
this.options = options;
}
train(data, labels) {
// Create your model
}
predict(testData) {
// Apply your model and return predicted label
return prediction;
}
}
```
###
[npm-image]: https://img.shields.io/npm/v/ml-cross-validation.svg?style=flat-square
[npm-url]: https://npmjs.org/package/ml-cross-validation
[travis-image]: https://img.shields.io/travis/mljs/cross-validation/master.svg?style=flat-square
[travis-url]: https://travis-ci.org/mljs/cross-validation
[download-image]: https://img.shields.io/npm/dm/ml-cross-validation.svg?style=flat-square
[download-url]: https://npmjs.org/package/ml-cross-validation