scikit-survival

Topics in the source

• scikit-survival: Survival Analysis in Python
• Overview
• When to Use This Skill
• Core Capabilities
• 1. Model Types and Selection
• Cox Proportional Hazards Models
• Ensemble Methods
• Survival Support Vector Machines

scikit-survival is a Python library for survival analysis built on top of scikit-learn. It provides specialized tools for time-to-event analysis, handling the unique challenge of censored data where some observations are only partially known. Survival analysis aims to establish connections between covariates and the time of an event, accounting for censored records (particularly right-censored data from studies where participants don't experience events during observation periods). Use this skill when: Performing survival analysis or time-to-event modeling Working with censored data (right-censored, left-censored, or interval-censored) Fitting Cox proportional hazards models (standard or penalized) Building ensemble survival models (Random Survival Forests, Gradient Boosting) Training Survival Support Vector Machines Evaluating survival model performance (concordance index, Brier score, time-dependent AUC) Estimating Kaplan-Meier or Nelson-Aalen curves Analyzing competing risks Preprocessing survival data or handling missing values in survival datasets Conducting any analysis using the scikit-survival library scikit-survival provides multiple model families, each suited for differe

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