scikit-survival

You can perform survival analysis in Python using scikit-survival, which fits censored time-to-event data via CoxPHSurvivalAnalysis, ensemble methods, and survival SVMs.

To model non-linear relationships in survival analysis, ensemble methods like Random Survival Forests and Gradient Boosting capture complex patterns that standard Cox models miss.

Evaluate Cox proportional hazards models using metrics like the concordance index, time-dependent AUC, and Brier score to measure discrimination and calibration of risk predictions.

Yes, survival analysis applies to reliability engineering and clinical prognosis by modeling time-to-event outcomes with censoring to estimate component failure risks and patient survival probabilities.

Handle margin-based risk scoring for survival data using FastSurvivalSVM and FastKernelSurvivalSVM estimators, which apply support vector machine approaches to censored time-to-event outcomes.

Use penalized alternatives like CoxnetSurvivalAnalysis instead of standard CoxPHSurvivalAnalysis when dealing with high-dimensional data to apply regularization and prevent overfitting in survival analysis.