{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/10"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"}],"enrichment":{"capability":"scikit-survival provides survival analysis models that handle censored data\u2014where event times are only partially observed\u2014built on top of scikit-learn's API and preprocessing tools.","skillfed_tags":["survival-analysis","time-to-event","censored-data"],"use_cases":["Clinical trials: predict time to patient recovery or adverse event while accounting for patients lost to follow-up.","Equipment reliability: model time-to-failure for machinery or components with incomplete failure records.","Customer churn: estimate time until customer departure when some customers remain active at study end.","Employee retention: analyze time-to-resignation with censored records for still-employed staff.","Loan default: predict time to default in credit portfolios where some loans mature without default."],"what_it_does":"scikit-survival is a Python library for survival analysis\u2014modeling the time until an event occurs when some observations are censored (only partially observed). Unlike standard machine learning, survival analysis must account for the fact that some training records contain incomplete event information, such as patients in a clinical study who remain event-free at the end of monitoring. The library builds on scikit-learn, allowing you to use familiar preprocessing, cross-validation, and model-selection workflows while fitting survival-specific models that handle this censoring.\n\nThe package requires modern Python (3.11 or later) and several numerical dependencies including numpy, scipy, pandas, and scikit-learn, plus optimization libraries ecos and osqp. Installation is straightforward on major platforms via conda-forge or pip, though a C/C++ compiler is needed for the compiled components.","worth_installing":"Yes, if you need survival analysis and accept GPL-3.0-or-later copyleft licensing. The package is actively maintained, well-integrated with scikit-learn, and handles the statistical complexities of censored data. Medium install friction (compiled dependencies, version constraints) is manageable on standard platforms. Not suitable for proprietary software without GPL compliance review."},"id":"scikit-survival","links":{"html":"https://skillfed.io/packages/scikit-survival","md":"https://skillfed.io/packages/scikit-survival.md","pypi":"https://pypi.org/project/scikit-survival/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-05","license_spdx":"GPL-3.0-or-later","license_treatment":"copyleft","name":"scikit-survival","python_support":"supports_current","summary":"Survival analysis built on top of scikit-learn"},"popularity":{"monthly_downloads":280054,"position":8115,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.28.0"}