C. Pachón García, C. Hernández-Pérez, P. Delicado, V. Vilaplana

We present SurvLIMEpy, an open-source Python package that implements the SurvLIME algorithm. This method allows to compute local feature importance for machine learning algorithms designed for modelling Survival Analysis data. Our implementation takes advantage of the parallelisation paradigm as all computations are performed in a matrix-wise fashion which speeds up execution time. Additionally, SurvLIMEpy assists the user with visualization tools to better understand the result of the algorithm. The package supports a wide variety of survival models, from the Cox Proportional Hazards Model to deep learning models such as DeepHit or DeepSurv.

Keywords: Interpretable Machine Learning,eXplainable Artificial Intelligence,Survival Analysis,Machine Learning


GT18.SOFTW1 Invited Session
November 7, 2023  4:50 PM
HC1: Canónigos Room 1

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