XGBoost in Node.js

A pure JavaScript implementation of XGBoost scoring

XGBoost v3.2.0 compiled to WebAssembly -- gradient-boosted trees in browsers and Node.js

Node.js bindings for XGBoost (Linux only)

XGBoost in Node.js

NodeJS implementation of decision tree, random forest, and XGBoost algorithms with comprehensive performance testing (Node.js 20+)

A wasm build of XGBoost with a convenient typescript interface. Runs with zero dependencies in both node and web environments.

A pure JavaScript implementation of XGBoost for both Node.js and browser environments

[![NPM version][npm-image]][npm-url] [![build status][travis-image]][travis-url] [![David deps][david-image]][david-url] [![npm download][download-image]][download-url]

Zero-dependency XGBoost inference for browser, edge, and Node runtimes.

XGBoost WebAssembly library for Node.js and Browser

AI-powered maintenance risk predictor for git repositories using XGBoost

AI-powered maintenance risk predictor for git repositories using XGBoost

A TypeScript toolkit for training, persisting, loading, and inferring high-performance decision-tree models using XGBoost

This package provides utilities for training and deploying machine learning models on AWS SageMaker. It supports multiple ML frameworks including PyTorch, TensorFlow, XGBoost, Scikit-learn, and HuggingFace.

Automated machine learning structure. Ensembles random forests, XGBoost, neural networks, AdaBoost, KNN, SVM together, handling data formatting ensembling, and running predictions for you. Feel free to tweak the settings if you want a lot of control, or j

East Data Science - ML/optimization platform functions for East (TypeScript definitions)

Supply-chain threat detection & response for npm & PyPI/Python

Build and deploy custom ML containers on AWS SageMaker with minimal configuration.

A lightweight nodejs library for machine learning algorithms