scikit-survival

You model censored time-to-event data by fitting Cox proportional hazards, ensemble, or support vector machine models, enabling precise prediction of survival times for medical research or engineering reliability datasets.

The best way to handle censored data for survival analysis is to create specific survival outcomes from your raw datasets, preprocess features, and apply models designed to account for right-censored time-to-event relationships.

You evaluate survival model performance by calculating concordance and Brier scores to measure discrimination and calibration across different censoring levels, using your preprocessed scikit-learn, numpy, and pandas datasets.

Yes, you can use ensemble methods on censored datasets for medical research. The toolkit supports fitting ensemble models alongside Cox models to analyze patient progression times and handle varied censoring levels.

Yes, you need scikit-learn, numpy, and pandas installed, as the survival analysis workflows depend on these libraries for compatible data preprocessing, feature manipulation, and model fitting.