Does this skill support advanced machine learning models?

Yes, it includes guidance for building and tuning Random Survival Forests, Gradient Boosting Survival Analysis, and Survival Support Vector Machines (SVMs).

What is scikit-survival used for?

It is a Python library for survival analysis, specifically designed to model the time until an event occurs while accounting for censored data where the exact event time is unknown.

Can I integrate these models with scikit-learn pipelines?

Absolutely. scikit-survival is built to be compatible with scikit-learn, and this skill provides patterns for using it with Pipeline, GridSearchCV, and cross-validation.

What are competing risks in survival analysis?

Competing risks occur when a subject can experience one of several different types of events, and the occurrence of one event prevents the others from happening. This skill provides methods to estimate cumulative incidence in these scenarios.

How does it handle censored data differently than standard regression?

It uses specialized objective functions and evaluation metrics like Uno's C-index and Integrated Brier Scores that specifically account for the bias introduced by right-censored observations.

Scikit-Survival Analysis

Name: Scikit-Survival Analysis
Author: pur3v4d3r

bypur3v4d3r

•

数据科学与机器学习

Implements comprehensive survival analysis and time-to-event modeling workflows using the scikit-survival library.

This skill equips Claude with specialized knowledge for performing survival analysis in Python, leveraging the scikit-survival library built on top of scikit-learn. It enables the creation of sophisticated time-to-event models—ranging from traditional Cox proportional hazards and penalized regression to advanced machine learning approaches like Random Survival Forests and Gradient Boosting. The skill provides expert guidance on handling the unique challenges of censored data, analyzing competing risks, and implementing specialized evaluation metrics such as Uno's C-index and Brier scores to ensure robust predictive performance in clinical, industrial, or business contexts.

主要功能

01Non-parametric estimation using Kaplan-Meier and Nelson-Aalen estimators

02Specialized survival modeling including Cox PH, Random Survival Forests, and Gradient Boosting

03Competing risks analysis for handling multiple mutually exclusive event outcomes

04Advanced evaluation metrics for censored data like Concordance Index and Time-Dependent AUC

051 GitHub stars

06Comprehensive data preprocessing for right, left, and interval-censored datasets

使用场景

01Modeling customer churn and time-to-attrition for subscription-based business models

02Analyzing patient survival rates and identifying risk factors in clinical and medical research

03Predicting time-to-failure and performing predictive maintenance for industrial equipment

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add pur3v4d3r/pur3-pkb-codebase scikit-survival

For use in Claude.ai and ChatGPT

主要功能

01Non-parametric estimation using Kaplan-Meier and Nelson-Aalen estimators

02Specialized survival modeling including Cox PH, Random Survival Forests, and Gradient Boosting

03Competing risks analysis for handling multiple mutually exclusive event outcomes

04Advanced evaluation metrics for censored data like Concordance Index and Time-Dependent AUC

051 GitHub stars

06Comprehensive data preprocessing for right, left, and interval-censored datasets

使用场景

01Modeling customer churn and time-to-attrition for subscription-based business models

02Analyzing patient survival rates and identifying risk factors in clinical and medical research

03Predicting time-to-failure and performing predictive maintenance for industrial equipment