js

SnugML/JS

JavaScript Machine Learning ES6 Module

CDN Usage

https://cdn.jsdelivr.net/gh/snugml/js@main/ml.mjs

Example

// Import library
const MLJS = 'https://cdn.jsdelivr.net/gh/snugml/js@main/ml.mjs'
import { DecisionTreeClassifier } from MLJS;

// Create an instance of the DecisionTreeClassifier
const model = new DecisionTree();

// Train the model using feature matrix (features) and label array (label)
model.fit(features, label);

// Make predictions using the trained model with the feature matrix
const yPredict = model.predict(features);

// Optionally, you can return or log the predictions
console.log(yPredict);

SnugML/JS is continuously being developed

JSDelivr changes may be slow. To use the latest version of the model, use:

const MLJS = 'https://snugml.github.io/js/ml.mjs'
import { DecisionTreeClassifier} from MLJS;

Available Exported Classes and Methods

# Class/Method Location (File) Description
1 LinearRegression /models/linear-model.mjs Class for performing linear regression.
2 PolynomialRegression /models/linear-model.mjs Class for performing polynomial regression.
3 GaussianNB /models/naive-bayes.mjs Class implementing the Naive Bayes classifier for Gaussian data.
4 DecisionTreeClassifier /models/tree.mjs Class for building and using decision trees.
5 MLPClassifier /models/neural-network.mjs Class implementing the MLP classifier.
6 KMeans /models/cluster.mjs Class for implementing the KMeans Cluster Classifier.
7 KNearestNeighbors /models/neighbors.mjs Class for implementing the KNearestNeighbors Classifier.
8 LabelEncoder /utils/preprocessing.mjs Class for encoding labels into numeric form.
9 trainTestSplit /utils/model-selection.mjs Function for splitting datasets into training and testing sets.
10 joinArrays /utils/model-selection.mjs Function for joining two or more arrays.
11 zip /utils/model-selection.mjs Function for zipping two or more arrays element-wise.
12 accuracyScore /utils/metrics.mjs Function to calculate the accuracy score of a model.
13 CSV /utils/data-analysis.mjs Class for read CSV and convert to arrays.

Examples

Linear Regression

Polynomial Regression

Gaussian Naive Bayes

Decision Tree

MLP Classifier Logic Gates

MLP Classifier

KMeans Cluster Classifier

KNearest Neighbors Classifier