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https://github.com/jsmlt/jsmlt

:factory: JavaScript Machine Learning Toolkit
https://github.com/jsmlt/jsmlt

classification javascript-library knn logistic-regression machine-learning random-forest svm

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:factory: JavaScript Machine Learning Toolkit

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# JSMLT

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[JSMLT](https://github.com/jsmlt/jsmlt/)



The JavaScript Machine Learning Toolkit, or JSMLT, is an open source JavaScript library for education in machine learning. It implements several well-known supervised learning algorithms in an understandable, modular and well-commented way. Furthermore, visualization examples are provided which allow you to explore the way different machine learning algorithms work. Ultimately, JSMLT is intended to provide students with a better learning experience when studying machine learning algorithms.



If you want to explore a visualization of the machine learning algorithms in JSMLT, check out [visualml.io](https://visualml.io). It provides an interactive environment for using JSMLT's algorithms.



# Getting started

This short guide will help you get started with JSMLT.



## Installation

> We're assuming you've got Node.js and npm installed. If you haven't, you should: download and install it from [nodejs.org](https://nodejs.org/en/).



To install JSMLT into your npm project via npm, run

```bash

npm install @jsmlt/jsmlt

```



## A simple example

In this small example, we're going to train an SVM on a small example dataset. The code example below starts with loading JSMLT, creating some dummy training and test data, and running an SVM classifier on it. It's pretty simple!



> If you want to run this example without having to set up anything by yourself, check out the [JSMLT examples repository](https://github.com/jsmlt/examples). It includes the example below, and requires no further setup: it's ready to run!



```js

// Import JSMLT library

var jsmlt = require('@jsmlt/jsmlt');



// Training data

train_X = [[-1,-1], [-1,1], [1,1], [1,-1]];

train_y = [0, 0, 1, 1];



// Testing data

test_X = [[1,2], [1,-2], [-1,-2], [-1,2]];



// Create and train classifier

var clf = new jsmlt.Supervised.SVM.SVM({

  kernel: new jsmlt.Kernel.Linear(),

});

clf.train(train_X, train_y);



// Make predictions on test data

console.log(clf.predict(test_X));

```



Running this simple example will output the classification result `[1,1,0,0]`, meaning it classified the first two points as 0, and the second two points as 1.



# API

> The entire API documentation can be found [here](https://visualml.io/jsmlt/docs/identifiers.html). You can also build the documentation locally by downloading and installing JSMLT and running `npm run-script build-documentation`: the documentation will then be available in the `docs` folder.



### Supervised learning algorithms (classifiers)

- Random Forest: [`JSMLT.Supervised.Trees.RandomForest`](https://visualml.io/jsmlt/docs/class/src/supervised/trees/random-forest.js~RandomForest.html)

- Decision Tree: [`JSMLT.Supervised.Trees.DecisionTree`](https://visualml.io/jsmlt/docs/class/src/supervised/trees/decision-tree.js~DecisionTree.html)

- Support Vector Machine (SVM): [`JSMLT.Supervised.SVM.SVM`](https://visualml.io/jsmlt/docs/class/src/supervised/svm/svm.js~SVM.html)

- Perceptron: [`JSMLT.Supervised.Linear.Perceptron`](https://visualml.io/jsmlt/docs/class/src/supervised/linear/perceptron.js~Perceptron.html)

- Neural Networks: [`JSMLT.Supervised.NeuralNetwork.FullyConnected`](https://visualml.io/jsmlt/docs/class/src/supervised/neural-network/fully-connected.js~FullyConnected.html)

- k-nearest neighbors: [`JSMLT.Supervised.Neighbors.KNN`](https://visualml.io/jsmlt/docs/class/src/supervised/neighbors/knn.js~KNN.html)

- Logistic Regression: [`JSMLT.Supervised.Neighbors.LogisticRegression`](https://visualml.io/jsmlt/docs/class/src/supervised/linear/logistic_regression.js~LogisticRegression.html)



### Unsupervised learning algorithms (clustering)

- k-means: [`JSMLT.Unsupervised.Neighbors.KMeans`](https://visualml.io/jsmlt/docs/class/src/unsupervised/neighbors/k-means.js~KMeans.html)



### Kernels

- Linear kernel: [`JSMLT.Kernel.LinearKernel`](https://visualml.io/jsmlt/docs/class/src/kernel/linear.js~LinearKernel.html)

- Gaussian (RBF) kernel: [`JSMLT.Kernel.GaussianKernel`](https://visualml.io/jsmlt/docs/class/src/kernel/gaussian.js~GaussianKernel.html)

- Polynomial kernel: [`JSMLT.Kernel.PolynomialKernel`](https://visualml.io/jsmlt/docs/class/src/kernel/gaussian.js~PolynomialKernel.html)

- Sigmoid kernel: [`JSMLT.Kernel.SigmoidKernel`](https://visualml.io/jsmlt/docs/class/src/kernel/gaussian.js~SigmoidKernel.html)



### Preprocessing

- Encode string or other type of labels to integers: [`JSMLT.Preprocessing.LabelEncoder`](https://visualml.io/jsmlt/docs/class/src/preprocessing/labelencoder.js~LabelEncoder.html)



### Model selection

- Data set splitting: [`JSMLT.ModelSelection.trainTestSplit`](https://visualml.io/jsmlt/docs/function/index.html#static-function-trainTestSplit)



### Datasets

- Iris dataset loading: [`JSMLT.Datasets.loadIris`](https://visualml.io/jsmlt/docs/function/index.html#static-function-loadIris)



### Validation

- Accuracy metric for validation: [`JSMLT.Validation.Metrics.accuracy`](https://visualml.io/jsmlt/docs/function/index.html#static-function-accuracy)

- AUROC metric for validation: [`JSMLT.Validation.Metrics.auroc`](https://visualml.io/jsmlt/docs/function/index.html#static-function-auroc)



### Classification boundaries

- Classification boundaries for trained classifier: [`JSMLT.Classification.Boundaries`](https://visualml.io/jsmlt/docs/class/src/classification/boundaries.js~Boundaries.html)



# Development

JSMLT is maintained by [Jesper van Engelen](https://github.com/engelen), and is in active development. It is currently not ready to be used in any production environments.