cross-validation

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[![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#k-fold_cross-validation)
- leave-p-out#Leave-p-out_cross-validation)

API documentation.

A list of the mljs supervised classifiers is available here in the supervised learning section, but you could also use your own. Cross validations methods return a ConfusionMatrix (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
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.
$3

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