Scikit Survival

Provides a comprehensive toolkit for analyzing censored data and modeling the time until specific events occur, built directly on the familiar scikit-learn framework. It enables the implementation of diverse survival models—from traditional Cox proportional hazards to advanced machine learning ensembles like Random Survival Forests and Gradient Boosting—while offering specialized evaluation metrics such as Uno's C-index and Brier scores to ensure predictive accuracy. This skill is essential for researchers and data scientists working with clinical trials, customer churn analysis, or industrial reliability engineering where observations are often incomplete.

主な機能

ユースケース