Gradient Boosting
Showing 13 of 13 projects
A scalable, portable, and distributed gradient boosting library for efficient machine learning across multiple languages and platforms.
A unified Python library for explaining any machine learning model's predictions using Shapley values from game theory.
A fast, distributed gradient boosting framework based on decision tree algorithms for ranking, classification, and other ML tasks.
A fast, distributed gradient boosting framework based on decision tree algorithms for ranking, classification, and other machine learning tasks.
A comprehensive collection of machine learning algorithms implemented exclusively in NumPy for educational purposes and prototyping.
A high-performance gradient boosting library with best-in-class handling of categorical features and support for CPU/GPU training.
An open-source, in-memory platform for distributed and scalable machine learning with support for a wide range of algorithms and big data technologies.
An open-source Python package for training interpretable glassbox models and explaining blackbox machine learning systems.
A toolkit for distributed machine learning featuring parameter server framework, topic modeling, gradient boosting, and word embedding.
A curated collection of research papers on decision, classification, and regression trees with implementations from top ML conferences.
A minimal benchmark comparing scalability, speed, and accuracy of popular open-source machine learning libraries for binary classification.
A Python library for probabilistic prediction using natural gradient boosting, built on scikit-learn.
Automated machine learning library for production and analytics, handling feature engineering, model selection, and hyperparameter optimization.
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