Decision Trees
Showing 22 of 22 projects
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 high-performance gradient boosting library with best-in-class handling of categorical features and support for CPU/GPU training.
A curated collection of research papers on decision, classification, and regression trees with implementations from top ML conferences.
A Python package for concise, transparent, and accurate predictive modeling with sklearn-compatible interpretable models.
A curated list of resources for random forest and other tree-based machine learning methods.
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 comprehensive PhD dissertation providing an in-depth theoretical and practical analysis of random forests, from algorithmic foundations to interpretability.
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 C# library for implementing behavior trees in game AI, providing a modular framework for creating complex NPC behaviors.
A pure Go library for making predictions with Gradient Boosting Regression Trees models from LightGBM, XGBoost, and scikit-learn.
A Scala framework for distributed supervised learning of decision tree ensemble models, inspired by Google's PLANET.
A tree ensemble machine learning method that delivers better results than gradient boosted decision trees on many datasets.
A high-performance, large-scale statistical machine learning library written in Common Lisp.
A Julia library providing a consistent API for common machine learning algorithms, designed for practitioners working with in-memory datasets.
A Go library for scoring machine learning models using PMML, supporting neural networks, decision trees, random forests, and gradient boosted models.
A Ruby gem for scoring predictive models using PMML, supporting decision trees, naive Bayes, logistic regression, random forests, and gradient boosted trees.
A Clojure library providing machine learning algorithms with simple APIs for data preprocessing and modeling.
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