Gradient Boosting
Showing 34 of 34 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 machine learning tasks.
A fast, distributed gradient boosting framework based on decision tree algorithms for ranking, classification, and other ML 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.
A curated collection of gradient boosting research papers with implementations from top machine learning conferences.
A universal model exchange and serialization format for decision tree forests, enabling cross-platform deployment.
Fast, flexible, multi-threaded ensembles of decision trees for machine learning in pure Go.
A fast GPU-accelerated library for training Gradient Boosting Decision Trees (GBDT) and Random Forests.
A hyperparameter-free gradient boosting machine with a simple budget parameter, built for high performance with Rust and bindings for Python and R.
A TensorFlow library for training, serving, and interpreting decision forest models like Random Forests and Gradient Boosted Trees.
An optimized distributed gradient boosting library for fast and accurate machine learning on large datasets.
A lightweight Python decision tree framework supporting ID3, C4.5, CART, CHAID, regression trees, gradient boosting, random forest, and AdaBoost with categorical feature support.
A pure Go library for making predictions with Gradient Boosting Regression Trees models from LightGBM, XGBoost, and scikit-learn.
Python implementation of the RuleFit algorithm for interpretable machine learning predictions using rule ensembles.
A tree ensemble machine learning method that delivers better results than gradient boosted decision trees on many datasets.
A Julia interface for XGBoost, providing efficient distributed gradient boosting for regression, classification, and ranking.
A Ruby interface to XGBoost, providing high-performance gradient boosting for machine learning tasks.
A Go library for scoring machine learning models using PMML, supporting neural networks, decision trees, random forests, and gradient boosted models.
A Ruby gem providing high-performance gradient boosting with LightGBM for machine learning tasks.
A comprehensive Java library for statistics, data mining, and machine learning with interactive notebook support.
A Ruby gem for scoring predictive models using PMML, supporting decision trees, naive Bayes, logistic regression, random forests, and gradient boosted trees.
Rust bindings for LightGBM, enabling gradient boosting for machine learning tasks in Rust.
A Node.js interface for running XGBoost models and making predictions.
A Go library using Cgo for blazing fast inference of CatBoost gradient boosting models.
Ruby bindings for XGBoost, enabling machine learning model inference in Ruby applications.
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