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XGBoost is a distributed machine learning library for implementing scalable gradient boosting decision trees used for regression, classification, and ranking. It functions as a predictive model framework and a cross-language toolkit, providing a core implementation with native bindings for Python, R, Java, Scala, and C++. The system is designed as a GPU-accelerated library that utilizes CUDA and NCCL to speed up the training of decision tree ensembles. It operates as a distributed framework capable of scaling training and prediction across multi-node clusters and GPU environments to process m
LightGBM is a high-performance machine learning framework designed for constructing gradient-boosted decision tree ensembles. It provides a platform for training classification, regression, and ranking models, with a focus on memory efficiency and large-scale distributed computing. The framework distinguishes itself through specialized algorithmic strategies, including leaf-wise tree growth and histogram-based decision learning, which prioritize convergence speed. It optimizes memory usage by bundling mutually exclusive features and employs gradient-based sampling to reduce training complexit
CatBoost is a gradient boosting machine learning library used to train decision tree ensembles for regression, classification, and ranking tasks. It functions as a high-performance framework that provides a categorical data processor for transforming non-numeric features, a distributed trainer for large-scale datasets, and GPU acceleration to speed up model construction. The library distinguishes itself through native handling of categorical data and text features, removing the need for manual encoding. It includes a specialized model interpretability tool that leverages SHAP values and featu
Apache MXNet is a deep learning framework and distributed machine learning library designed for training and deploying neural networks across distributed systems, mobile devices, and hardware accelerators. It functions as a cross-platform runtime and a dynamic dataflow scheduler that optimizes neural network execution. The framework provides a multi-language API, enabling the development of machine learning models using Python, R, Julia, Scala, Go, and JavaScript. It supports high-performance model training and the scaling of workloads across multiple GPUs and machines. The system covers cap
ThunderGBM: Fast GBDTs and Random Forests on GPUs
The main features of xtra-computing/thundergbm are: General Machine Learning, Gradient Boosting, Machine Learning Frameworks.
Open-source alternatives to xtra-computing/thundergbm include: microsoft/lightgbm — LightGBM is a high-performance machine learning framework designed for constructing gradient-boosted decision tree… dmlc/xgboost — XGBoost is a distributed machine learning library for implementing scalable gradient boosting decision trees used for… catboost/catboost — CatBoost is a gradient boosting machine learning library used to train decision tree ensembles for regression,… apple/turicreate — This project is an automated machine learning framework and toolkit designed for training and tuning custom models for… apache/mahout — Apache Mahout - an environment for quickly creating scalable, performant machine learning applications. amazaspshumik/sklearn-bayes — Python package for Bayesian Machine Learning with scikit-learn API.