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scikit-survival

Analyze censored survival data and build time-to-event models with scikit-survival.

321|26|Updated Mar 10, 2026
Search Tags:#model-evaluation#machine-learning#scikit-survival#survival-analysis#time-to-event#data-preprocessing#cox-models
One-click install
npx skills add https://github.com/mkurman/tamux --skill scikit-survival-mkurman
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scikit-survival
Source: https://github.com/mkurman/tamux/tree/main/skills/scientific-skills/scikit-survival
Command: npx skills add https://github.com/mkurman/tamux --skill scikit-survival-mkurman

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

scikit-survival provides survival analysis capabilities for censored time-to-event data, built on top of scikit-learn to enable robust modeling in Python.

Core Features & Use Cases

  • Cox proportional hazards models (CoxPH, Coxnet) for interpretable risk factors
  • Ensemble and SVM-based survival models (RandomSurvivalForest, GradientBoosting, FastSurvivalSVM, FastKernelSurvivalSVM)
  • Data preprocessing, feature handling, and evaluation metrics compatible with scikit-learn pipelines
  • Support for survival curve estimation and competing risks analyses
  • Use cases include biomedical research, epidemiology, and risk prediction across small to large datasets

Quick Start

Install scikit-survival and run a simple CoxPH model on your dataset to get started.

Frequently Asked Questions about scikit-survival

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build survival analysis models with censored time-to-event data in Python?▼

Survival analysis models for censored time-to-event data can be built using scikit-survival's specialized estimators on top of scikit-learn. This enables Cox proportional hazards modeling, survival curve estimation, and risk prediction across biomedical datasets.

Can I use scikit-learn pipelines for survival data preprocessing and model evaluation?▼

Yes, scikit-survival provides data preprocessing, feature handling, and evaluation metrics that integrate directly with standard scikit-learn pipelines, allowing seamless transformation and scoring of survival data.

What are the best machine learning models for competing risks and time-to-event analysis?▼

For competing risks and time-to-event analysis, ensemble and SVM-based survival models like RandomSurvivalForest, GradientBoosting, and FastSurvivalSVM offer robust risk prediction across small to large epidemiological datasets.

Does Cox proportional hazards modeling work with high-dimensional survival datasets?▼

Yes, scikit-survival includes Coxnet and CoxPH models specifically designed for interpretable risk factor analysis. These models scale to handle high-dimensional biomedical and epidemiological datasets effectively.

When should I use SVM-based survival models instead of Cox models for risk prediction?▼

SVM-based survival models like FastSurvivalSVM are ideal when you need flexible, non-linear risk prediction on large datasets, whereas Cox models provide interpretable risk factors for biomedical research and epidemiological studies.

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