Data Science

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.

IPython-based environment for reproducible machine learning research with unified wrappers for multiple ML libraries.

A scikit-learn compatible Python module for multi-label classification tasks.

A Python library for structured learning and prediction with max-margin methods and a scikit-learn compatible interface.

A scikit-learn compatible classifier that produces human-interpretable decision rules instead of black box models.

Python implementation of the RuleFit algorithm for interpretable machine learning predictions using rule ensembles.

A Python library for building Generalized Additive Models (GAMs) with a scikit-learn-like API, emphasizing interpretability and performance.

A Python package for uplift modeling and causal inference using machine learning algorithms to estimate treatment effects.

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 fast GPU-accelerated library for training Gradient Boosting Decision Trees (GBDT) and Random Forests.

A Python library for probabilistic prediction using natural gradient boosting, built on scikit-learn.

A TensorFlow library for training, serving, and interpreting decision forest models like Random Forests and Gradient Boosted Trees.

A Python library for stacked generalization (ensemble learning) that supports scikit-learn, XGBoost, and Keras models with out-of-fold prediction saving.

A Python package for stacking (stacked generalization) with both functional and scikit-learn compatible APIs.

A Python implementation of Factorization Machines for recommendation and classification tasks using stochastic gradient descent with adaptive regularization.

A Python library implementing Factorization Machines with a scikit-learn compatible API for regression, classification, and ranking tasks.

TensorFlow implementation of arbitrary order (≥2) Factorization Machines for classification and regression tasks.

A fast and versatile implementation of support vector machines with integrated hyper-parameter selection and support for multiple learning scenarios.

A scikit-learn compatible Python implementation of the Relevance Vector Machine for sparse Bayesian learning.

A fast Support Vector Machine (SVM) library that leverages GPUs and multi-core CPUs for high-performance machine learning.