Gradient Boosting
Showing 30 of 30 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 TensorFlow library for training, serving, and interpreting decision forest models like Random Forests and Gradient Boosted Trees.
A hyperparameter-free gradient boosting machine with a simple budget parameter, built for high performance with Rust and bindings for Python and R.
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.
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