skills.rest
jasrajtulsi
jasrajtulsiCOMMUNITY
Explore all skills by this author →
GitHub

scikit-survival

Analyze censored survival datasets and estimate time-to-event risk with scikit-survival models.

Updated Jul 1, 2026
Search Tags:#survival analysis#brier score#cox model#time-to-event#censored data#random survival forest#competing risks
One-click install
npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill scikit-survival-jasrajtulsi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scikit-survival
Source: https://github.com/jasrajtulsi/GRAD-SCOPE/tree/main/.claude/skills/scikit-survival
Command: npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill scikit-survival-jasrajtulsi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

It helps you analyze censored time-to-event data when standard regression and classification methods cannot correctly account for incomplete outcomes.

Core Features & Use Cases

  • Fits Cox proportional hazards, penalized Cox, random survival forests, gradient boosting, and survival SVM models for survival prediction.
  • Prepares survival outcomes, encodes features, standardizes inputs, and supports evaluation with concordance index, time-dependent AUC, and Brier score.
  • Handles practical research scenarios such as clinical prognosis, competing risks, feature selection, and comparing multiple survival models on the same dataset.

Quick Start

Ask for a complete survival analysis workflow on your dataset, including outcome construction, preprocessing, model fitting, and evaluation.

Frequently Asked Questions about scikit-survival

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

FAQPage Schema
How do I analyze censored time-to-event data when standard regression cannot account for incomplete outcomes?▼

Survival analysis with censored data requires models like Cox proportional hazards or random survival forests to correctly handle incomplete time-to-event outcomes. This workflow prepares survival outcomes, encodes features, fits models, and evaluates performance using censoring-aware metrics.

What metrics should I use to evaluate a survival model on censored data?▼

Evaluating survival models on censored data requires censoring-aware metrics such as the concordance index, time-dependent AUC, and Brier score. These metrics measure predictive accuracy and calibration specifically for incomplete time-to-event outcomes.

Can I use random survival forests and gradient boosting for clinical prognosis?▼

Clinical prognosis workflows can use random survival forests, gradient boosting, and survival SVMs to estimate time-to-event risk. These models handle structured event-time outcomes and feature preprocessing to predict patient survival probabilities.

How do I handle competing risks in a survival analysis dataset?▼

Competing risks in survival datasets require specialized time-to-event modeling to account for multiple possible event types. This workflow supports competing risks scenarios alongside feature selection and model comparison within scikit-survival compatible procedures.

When should I use penalized Cox models instead of standard survival models?▼

Penalized Cox models are used for survival prediction when dealing with high-dimensional features or multicollinearity in time-to-event data. They extend standard Cox proportional hazards models by adding regularization to improve feature selection and prevent overfitting.

Related Skills

Explore More Data & Analytics Skills→
dralkhdralkh
74

scikit-survival

Model censored survival data with scikit-survival Cox and random survival forest methods.

Community
Advanced
LogauaEngstromLogauaEngstrom
18

scikit-survival

Fit survival models and evaluate censored time-to-event data with scikit-survival.

Community
Advanced
sanand0sanand0
8

scikit-survival

Fit Cox models and survival analysis pipelines with scikit-learn integration.

Community
Advanced
shushuznshushuzn
4

scikit-survival

Build censored-data survival models with scikit-survival for Cox, ensemble, and SVM approaches.

Community
Intermediate