"One of the biggest pain points for users seems to be understanding which metric can be used to evaluate the performance of a given estimator. The user guide now summarizes the different options. Which Performance Metrics Exist? The performance metrics for evaluating survival models can be broadly divided into three groups: Concordance Index (C-index): Measures the rank correlation between predicted risk scores and observed event times. Two implementations are available in scikit-survival:"
"Cumulative/Dynamic Area Under the ROC Curve (AUC): Extends the AUC to survival data, quantifying how well a model distinguishes subjects who experience an event by a given time from those who do not. It can handle time-dependent risk scores and is implemented in cumulative_dynamic_auc(). Brier Score: An extension of the mean squared error to right-censored data. The Brier score assesses both discrimination and calibration based on a model's estimated survival functions."
scikit-survival 0.25.0 adds support for scikit-learn 1.7 while maintaining compatibility with 1.6 and delivers a complete API documentation overhaul to improve clarity and consistency. The user guide summarizes performance metrics for survival models, grouping them into concordance index (C-index), cumulative/dynamic AUC, and the Brier score. cumulative_dynamic_auc(), brier_score(), and integrated_brier_score() are available for time-dependent AUC and Brier computations, including integrated measures over time. Survival estimators provide predict() that returns either unit-less risk scores or predicted event times. Higher risk scores indicate increased event risk and are meaningful primarily for ranking samples.
Read at Sebastian Polsterl
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