Ranking
Showing 14 of 14 projects
A repository of examples, utilities, and best practices for building and deploying production-ready recommendation systems.
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 machine learning library designed for human interpretability, featuring debuggable models and a feature transform language.
A TensorFlow library for Learning-to-Rank (LTR) techniques, providing loss functions, metrics, and models for ranking tasks.
A Python library implementing Factorization Machines with a scikit-learn compatible API for regression, classification, and ranking tasks.
A PyTorch framework for training neural learning-to-rank models with flexible loss functions and scoring architectures.
A TensorFlow library for training, serving, and interpreting decision forest models like Random Forests and Gradient Boosted Trees.
A Julia interface for XGBoost, providing efficient distributed gradient boosting for regression, classification, and ranking.
A Python library providing comprehensive metrics for fair and thorough evaluation of recommender systems.
A machine learning and optimization framework for Objective-C and Swift, focused on regression and multi-objective evolutionary algorithms.
A Go library implementing the Weighted PageRank algorithm for graph analysis and ranking.
A ranked list of the most starred Crystal web frameworks on GitHub, automatically updated.
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