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jsmlt
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:factory: JavaScript Machine Learning Toolkit
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# JSMLT [](https://www.npmjs.com/package/@jsmlt/jsmlt) [](https://www.npmjs.com/package/@jsmlt/jsmlt) [](https://github.com/jsmlt/jsmlt) [<img alt="JSMLT" src="https://avatars0.githubusercontent.com/u/31863813?v=4&s=160" height="160px" align="right"/>](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.
