Performs advanced survival analysis and time-to-event modeling using the scikit-survival Python library.
The scikit-survival skill empowers Claude to handle complex time-to-event datasets where observations are frequently censored. It provides a comprehensive framework for implementing everything from traditional Cox Proportional Hazards models to modern machine learning approaches like Random Survival Forests and Gradient Boosting. By integrating specialized evaluation metrics such as the Concordance Index and Brier score, this skill ensures that your survival models are both accurate and properly validated for real-world applications in medicine, finance, or engineering.
Key Features
01Comprehensive survival modeling including CoxPH, Random Survival Forests, and SVMs
02Advanced evaluation metrics like Uno's C-index and time-dependent AUC
03Specialized handling for right, left, and interval-censored data
04Seamless integration with scikit-learn pipelines and cross-validation
050 GitHub stars
06Competing risks analysis for mutually exclusive event types
Use Cases
01Predicting patient survival rates and risk factors in clinical research
02Analyzing customer churn and subscription longevity for SaaS businesses
03Estimating time-to-failure for industrial machinery and equipment
