{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/4"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"}],"enrichment":{"capability":"CatBoost is a gradient boosting library that trains decision-tree ensembles for classification, regression, and ranking, with built-in support for categorical features and GPU acceleration.","skillfed_tags":["gradient-boosting","categorical-features","gpu-accelerated"],"use_cases":["Train gradient boosting models on tabular data with mixed numerical and categorical features without manual encoding","Accelerate model training on GPU or multi-GPU systems for large datasets","Build distributed gradient boosting pipelines using Apache Spark integration","Generate feature importance rankings and visualizations for model interpretation","Deploy pre-trained CatBoost models in production applications via the C++ API"],"what_it_does":"CatBoost is a gradient boosting framework that builds ensembles of decision trees for supervised learning tasks. It is designed to handle both numerical and categorical features natively, reducing the need for manual feature engineering. The library includes GPU and multi-GPU training support, distributed training via Apache Spark, and built-in visualization tools for model interpretation.\n\nThe package depends on numpy, pandas, scipy, matplotlib, plotly, graphviz, and six. It ships as pre-compiled wheels for multiple Python versions and platforms (macOS, Linux, Windows). Installation is straightforward but medium-friction due to the compiled nature of the library. CatBoost is actively maintained with recent releases and is suitable for production use.","worth_installing":"Yes. CatBoost is production-stable, actively maintained, permissively licensed, and free of known vulnerabilities. Install it if you work with tabular data and want gradient boosting with native categorical feature support, GPU acceleration, or distributed training. The medium install friction is typical for compiled ML libraries and is not a barrier."},"id":"catboost","links":{"html":"https://skillfed.io/packages/catboost","md":"https://skillfed.io/packages/catboost.md","pypi":"https://pypi.org/project/catboost/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-02-18","license_spdx":null,"license_treatment":"permissive","name":"catboost","python_support":"unspecified","summary":"CatBoost Python Package"},"popularity":{"monthly_downloads":6227476,"position":1952,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.2.10"}