imatge-upc/SurvLIMEpy
Local interpretability for survival models
When analyzing 'time-to-event' data in fields like medicine or engineering, you often want to understand what factors most influence an outcome (like patient survival or machine failure). SurvLIMEpy helps you interpret why a specific prediction was made by a survival model. It takes your survival model and a particular data point, then outputs an explanation showing which features were most important for that individual prediction.
No commits in the last 6 months. Available on PyPI.
Use this if you need to understand the individual feature contributions that led to a specific prediction from your survival analysis model.
Not ideal if you are looking for a new survival model to train, or if you need explanations for standard classification or regression models.
Stars
24
Forks
4
Language
Python
License
GPL-3.0
Last pushed
May 27, 2024
Commits (30d)
0
Dependencies
8
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