Purpose Machine Learning
10 projects
An open-source, low-code Python library that automates end-to-end machine learning workflows.
A unified and efficient machine learning toolbox with C++ core and multi-language interfaces, developed since 1999.
A high-performance, easy-to-use, and scalable machine learning package for linear models, factorization machines, and field-aware factorization machines.
A suite of GPU-accelerated machine learning algorithms with scikit-learn compatible APIs for 10-50x faster performance on large datasets.
A modular active learning framework for Python built on scikit-learn, enabling rapid creation of custom workflows.
A fast, header-only C++ machine learning library with bindings for Python, R, Julia, and Go.
A modern C++ toolkit for machine learning, computer vision, and data analysis applications.
A Python library providing extensions and utilities for data science and machine learning tasks.
HyperLearn provides 2-2000x faster machine learning algorithms with 50% less memory usage, optimized for all hardware.
A Python package for uplift modeling and causal inference using machine learning algorithms to estimate treatment effects.
Gradient Boosting
3 projects
A scalable, portable, and distributed gradient boosting library for efficient machine learning across multiple languages and platforms.
A high-performance gradient boosting library with best-in-class handling of categorical features and support for CPU/GPU training.
A Python library for probabilistic prediction using natural gradient boosting, built on scikit-learn.
