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#2 XGBoost
Optimized distributed gradient boosting library designed to be highly efficient, flexible and portable.
#3 CatBoost
Gradient boosting on decision trees with native support for categorical features and GPU acceleration.
TensorFlow extension that brings state‑of‑the‑art decision forest algorithms (including gradient boosting) into the TensorFlow ecosystem.
Classic gradient boosting implementation within the widely‑used scikit‑learn library.
Fast, scalable GBM implementation with automatic hyper‑parameter tuning and model interpretability tools.
Cloud‑based AutoML service that automatically selects and tunes gradient‑boosting models among many algorithms.
Managed service offering built‑in XGBoost and linear learners with hyperparameter optimization.
Managed boosted‑tree models (including XGBoost) integrated with Google Cloud’s data pipelines.
Julia’s machine‑learning framework offering gradient boosting via XGBoost, LightGBM, and native implementations.
Distributed gradient‑boosted tree implementation that scales to big data on Apache Spark clusters.
Parallel gradient boosting that works with Dask arrays and dataframes for out‑of‑core computation.
#13 CatBoost GPU
GPU‑accelerated version of CatBoost delivering faster training on large datasets.
Native Java/Scala bindings for XGBoost, enabling integration with JVM‑based pipelines.
#15 FastAI Tabular
High‑level library built on PyTorch that includes tabular models with gradient boosting‑style ensembles.
#16 PyCaret
Low‑code machine‑learning library that automatically compares and tunes gradient‑boosting models (XGBoost, LightGBM, CatBoost).
#17 MLflow
Open‑source platform for managing the ML lifecycle; can log and serve LightGBM alternatives like XGBoost or CatBoost.
Hyperparameter‑tuning library for Keras that can be combined with TensorFlow Decision Forests for automated model search.
#19 Vowpal Wabbit
Fast online learning system that includes boosted tree algorithms for large‑scale problems.
#20 Hummingbird
Translates trained tree‑based models (including LightGBM, XGBoost, CatBoost) into tensor computations for GPU inference.
#21 MindsDB
AI layer for databases that can run gradient‑boosted models directly inside SQL queries.
AutoML toolkit that automatically ensembles multiple gradient‑boosting models (LightGBM, CatBoost, XGBoost) for best performance.