scikit-survival

Name: scikit-survival
Availability: InStock
Author: OwnLab

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

21|1|Updated Mar 19, 2026

Search Tags:#machine-learning #scikit-survival #survival-analysis #time-to-event #censoring #cox-models #random-survival-forest

Ready to run

npx skills add https://github.com/OwnLabAI/ownlab --skill scikit-survival-ownlabai

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: scikit-survival
Source: https://github.com/OwnLabAI/ownlab/tree/main/mart/skills/scientific-skills/scikit-survival
Command: npx skills add https://github.com/OwnLabAI/ownlab --skill scikit-survival-ownlabai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Survival analysis in Python with censoring is supported by scikit-survival, providing a comprehensive toolkit for time-to-event modeling and evaluation.

Core Features & Use Cases

Support for CoxPHSurvivalAnalysis, CoxnetSurvivalAnalysis, and IPCRidge for classic and regularized regression in survival analysis
Ensemble methods including RandomSurvivalForest, GradientBoostingSurvivalAnalysis, and ExtraSurvivalTrees for nonlinear relationships
Survival SVMs such as FastSurvivalSVM, FastKernelSurvivalSVM, and HingeLossSurvivalSVM for margin-based learning
Comprehensive preprocessing, evaluation metrics (concordance index, time-dependent AUC, Brier score), and competing risks workflows
Practical workflows spanning data loading, model selection, evaluation, and interpretation

Quick Start

Install scikit-survival, load a dataset, fit a model (for example CoxPHSurvivalAnalysis), and evaluate its performance.

Frequently Asked Questions about scikit-survival

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

FAQPage Schema

How do I build a survival analysis model with censored data in Python?▼

Build survival analysis models with censored data in Python by using scikit-survival. Fit models like CoxPHSurvivalAnalysis or RandomSurvivalForest on time-to-event datasets, leveraging integration with pandas and scikit-learn for preprocessing.

What evaluation metrics are used for time-to-event models?▼

Time-to-event models are evaluated using metrics like the concordance index (C-index), time-dependent AUC, and Brier score. These metrics measure ranking performance and prediction accuracy over time for censored survival data.

Can I use ensemble methods like random survival forest for nonlinear survival data?▼

Yes, you can model nonlinear survival data using ensemble methods like RandomSurvivalForest, GradientBoostingSurvivalAnalysis, and ExtraSurvivalTrees to capture complex relationships that classic regression models miss.

Does scikit-survival support competing risks workflows?▼

Scikit-survival supports competing risks workflows alongside comprehensive preprocessing and evaluation. It provides specialized models and metrics to handle multiple distinct failure types within time-to-event analysis.

When should I use Survival SVMs instead of Cox models for time-to-event analysis?▼

Use Survival SVMs like FastSurvivalSVM for margin-based learning when you need to optimize ranking performance directly, instead of using classic Cox models that rely on proportional hazards assumptions for survival analysis.