catboost and awesome-gradient-boosting-papers
The first is a production gradient boosting library implementation while the second is a research paper collection with code examples, making them **complements** — practitioners use CatBoost to implement the algorithms they learn about from the curated papers.
About catboost
catboost/catboost
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
This tool helps data scientists and machine learning engineers build accurate predictive models quickly. You input your structured datasets, which can include both numerical and descriptive (categorical) information, and it outputs a high-performing predictive model for tasks like classification, regression, or ranking. It's designed for professionals who need robust models for forecasting, anomaly detection, or personalized recommendations.
About awesome-gradient-boosting-papers
benedekrozemberczki/awesome-gradient-boosting-papers
A curated list of gradient boosting research papers with implementations.
This list provides a comprehensive collection of research papers and their accompanying code implementations specifically focused on gradient and adaptive boosting techniques. It helps researchers and practitioners in various AI fields stay updated on the latest advancements and reproduce results. You can find papers from top conferences in machine learning, computer vision, natural language processing, and data science, making it a valuable resource for those building or studying advanced predictive models.
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