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lib-scikit-survival

Official

Master survival analysis with scikit-survival.

Data & Analytics#statistics#survival analysis#predictive modeling#scikit-learn#time-to-event#censored data
Authorbiomaps-infra
Version1.0.0
Installs0

System Documentation

What problem does it solve?

This Skill provides a comprehensive toolkit for performing survival analysis and time-to-event modeling in Python, enabling you to analyze censored data and build predictive models for time-based outcomes.

Core Features & Use Cases

  • Model Fitting: Fit various survival models including Cox Proportional Hazards, Random Survival Forests, Gradient Boosting, and Survival SVMs.
  • Data Handling: Preprocess survival data, create survival outcomes, and handle censoring.
  • Evaluation: Assess model performance using metrics like Concordance Index, AUC, and Brier Score.
  • Use Case: Analyze patient data to predict time to disease recurrence, accounting for censored observations, and evaluate the impact of different treatments.

Quick Start

Use the lib-scikit-survival skill to fit a CoxPHSurvivalAnalysis model to the provided data.

Dependency Matrix

Required Modules

None required

Components

references

💻 Claude Code Installation

Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.

Please help me install this Skill:
Name: lib-scikit-survival
Download link: https://github.com/biomaps-infra/blender-opencode/archive/main.zip#lib-scikit-survival

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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