survival/

Survival analysis algorithms.

Time-to-event modelling where some subjects are right-censored: the event of interest (failure, churn, relapse) had not happened when observation stopped, so their recorded time is a lower bound on the true event time. Input is therefore (X, time, event) rather than (X, y), with event marking which time values are observed events and which are censoring times.

Algorithms

KaplanMeierEstimator: The non-parametric product-limit estimate of the

survival curve. No covariates; the population-level baseline.

NelsonAalenEstimator: The non-parametric cumulative-hazard estimate.

Useful as the hazard analogue of Kaplan-Meier and as a building block for leaf hazards in RandomSurvivalForest.

CoxPHSurvival: The Cox proportional-hazards model. Semiparametric:

estimates covariate effects (coefficients_) without specifying the baseline hazard, with optional L2 regularisation.

RandomSurvivalForest: An ensemble of censoring-aware survival trees.

Metrics

concordance_index: Harrell's C-index, the share of comparable pairs the

model ranks correctly.

integrated_brier_score: IPCW-weighted Brier score integrated over a grid

of times.

logrank_test: two-sample log-rank test of whether two survival curves

differ.

Notes

Risk scores are oriented so that a larger value means an earlier expected event. That is the convention Survival documents, and every model here follows it.