This post is to be read in conjunction with https://thierrymoudiki.github.io/blog/2025/02/10/python/Benchmark-QRT-Cube and https://thierrymoudiki.github.io/blog/2024/12/15/python/agnostic-survival-analysis.

Survival analysis is a group of Statistical/Machine Learning (ML) methods for predicting the time until an event of interest occurs. Examples of events include:

• death
• failure
• recovery
• default
• etc.

And the event of interest can be anything that has a duration:

• the time until a machine breaks down
• the time until a customer buys a product
• the time until a patient dies
• etc.

The event can be censored, meaning that it has’nt occurred for some subjects at the time of analysis.

In this post, I show how to use scikit-learn, xgboost, lightgbm in R, in conjuction with Python package survivalist for probabilistic survival analysis. The probabilistic part is based on conformal prediction and Bayesian inference, and graphics represent the out-of-sample ML survival function.