sparsesurv: a Python package for fitting sparse survival models via knowledge distillation.
Journal:
Bioinformatics (Oxford, England)
Published Date:
Sep 2, 2024
Abstract
MOTIVATION: Sparse survival models are statistical models that select a subset of predictor variables while modeling the time until an event occurs, which can subsequently help interpretability and transportability. The subset of important features is often obtained with regularized models, such as the Cox Proportional Hazards model with Lasso regularization, which limit the number of non-zero coefficients. However, such models can be sensitive to the choice of regularization hyperparameter.