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scikit-survival

Model time-to-event data with censoring using scikit-learn survival analysis tools.

22|4|Updated May 25, 2026
Search Tags:#machine-learning#scikit-survival#survival-analysis#time-to-event#hazard-analysis#censoring#cox-models
One-click install
npx skills add https://github.com/crazymsn/academic-skills --skill scikit-survival-crazymsn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scikit-survival
Source: https://github.com/crazymsn/academic-skills/tree/main/academic-skills/scikit-survival
Command: npx skills add https://github.com/crazymsn/academic-skills --skill scikit-survival-crazymsn

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

scikit-survival provides survival analysis tools built on scikit-learn to model time-to-event data with censoring, enabling robust hazard modeling, competing risks, and time-dependent predictions.

Core Features & Use Cases

  • CoxPHSurvivalAnalysis for standard survival analysis with interpretable hazards
  • Penalized Cox models (CoxnetSurvivalAnalysis, IPCRidge) for high-dimensional feature selection and regularization
  • Ensemble methods (RandomSurvivalForest, GradientBoostingSurvivalAnalysis, ExtraSurvivalTrees) for capturing complex non-linear relationships
  • Survival Support Vector Machines (FastSurvivalSVM, FastKernelSurvivalSVM) for non-linear decision boundaries
  • Comprehensive data preprocessing, evaluation metrics, and tight integration with scikit-learn

Quick Start

Install the library and fit a CoxPHSurvivalAnalysis model on a sample dataset to obtain risk scores and evaluate model performance.

Frequently Asked Questions about scikit-survival

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

FAQPage Schema
How do I perform survival analysis on time-to-event data with censoring in Python?▼

Survival analysis on censored time-to-event data can be performed using scikit-survival, which builds on scikit-learn to provide models like CoxPHSurvivalAnalysis for robust hazard modeling and time-dependent predictions.

What machine learning models are available for hazard analysis and competing risks?▼

Hazard analysis and competing risks can be modeled using CoxPHSurvivalAnalysis, RandomSurvivalForest, GradientBoostingSurvivalAnalysis, and survival SVMs to capture both linear and non-linear relationships in time-to-event data.

Can I use scikit-learn preprocessing and evaluation utilities with survival models?▼

Yes, survival models integrate tightly with scikit-learn, allowing you to use standard data preprocessing and evaluation metrics alongside algorithms like CoxnetSurvivalAnalysis and IPCRidge for high-dimensional feature selection.

When should I use penalized Cox models versus ensemble methods for survival prediction?▼

Use penalized Cox models like CoxnetSurvivalAnalysis for high-dimensional feature selection and regularization, while ensemble methods like RandomSurvivalForest are better suited for capturing complex non-linear relationships in time-to-event data.

Does scikit-survival support non-linear decision boundaries for time-to-event predictions?▼

Yes, non-linear decision boundaries for time-to-event predictions are supported through Survival Support Vector Machines like FastSurvivalSVM and FastKernelSurvivalSVM, as well as ExtraSurvivalTrees ensemble methods.

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