scikit-survival

What problem does it solve?

This Skill empowers users to perform sophisticated survival analysis and time-to-event modeling in Python, overcoming the complexities of censored data and enabling robust predictions.

Core Features & Use Cases

• Model Diverse Survival Scenarios: Fit Cox proportional hazards models, Random Survival Forests, Gradient Boosting models, and Survival SVMs.
• Handle Censored Data: Accurately analyze data where the exact event time is unknown.
• Evaluate Model Performance: Utilize metrics like C-index, time-dependent AUC, and Brier score for comprehensive assessment.
• Use Case: A researcher studying patient outcomes after a new treatment can use this Skill to build a model that predicts survival time, accounts for patients who dropped out of the study, and identifies key prognostic factors.

Quick Start

Use the scikit-survival skill to fit a Cox proportional hazards model to the breast cancer dataset and evaluate its performance using the concordance index.