scikit-survival-analysis
scikit-survival -- Survival Analysis
Overview
scikit-survival is a Python library for time-to-event analysis built on scikit-learn. It handles right-censored data (observations where the event has not yet occurred) using Cox models, ensemble methods, survival SVMs, and non-parametric estimators. All models follow the scikit-learn fit/predict API and integrate with Pipelines, cross-validation, and GridSearchCV.
When to Use
- Modeling time-to-event outcomes with right-censored data (clinical trials, reliability)
- Fitting Cox proportional hazards models (standard or elastic net penalized)
- Building ensemble survival models (Random Survival Forest, Gradient Boosting)
- Training survival SVMs for margin-based learning on medium-sized datasets
- Evaluating survival predictions with censoring-aware metrics (C-index, Brier score, AUC)
- Estimating non-parametric survival curves (Kaplan-Meier, Nelson-Aalen)
- Analyzing competing risks with cumulative incidence functions
- High-dimensional survival data with automatic feature selection (CoxNet L1/L2)
- For simpler parametric models (Weibull, log-normal AFT) or statistical tests (log-rank), use
lifelines - For deep learning survival models, use
pycoxortorchlife
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