scikit-survival

Survival analysis on censored data fits time-to-event models like CoxPH, RandomSurvivalForest, and SurvivalSVM using scikit-survival. It guides preprocessing, model selection, and evaluation with metrics like the concordance index and Brier score.

Yes, survival models are evaluated using concordance index, Uno's C-index, time-dependent AUC, and Brier score. The toolkit provides training/test split and cross-validation workflows to measure predictive performance on censored outcomes.

Competing risks are handled by modeling cumulative incidence functions and cause-specific hazards. This approach isolates event-specific risks when multiple failure types exist, ensuring accurate time-to-event predictions for distinct outcomes.

Yes, survival analysis models integrate directly with scikit-learn pipelines to streamline end-to-end workflows. Data loading and preprocessing steps connect seamlessly with fitting Cox models, survival forests, and survival SVMs.

Use RandomSurvivalForest when capturing complex non-linear relationships in censored data, whereas CoxPHSurvivalAnalysis assumes proportional hazards. Both evaluate time-to-event outcomes but differ in underlying assumptions about hazard rates.

FastSurvivalSVM requires structured Surv objects to distinguish censored observations from event occurrences. Proper data handling ensures the SVM correctly accounts for incomplete event times during model training.