Utility library to do cross validation with supervised classifiers.

Cross-validation methods:

• k-fold
• leave-p-out

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.

npm i -s ml-cross-validation

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();

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.

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();

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.

class MyClassifier {
  constructor(options) {
    this.options = options;
  }
  train(data, labels) {
    // Create your model
  }
  predict(testData) {
    // Apply your model and return predicted label
    return prediction;
  }
}