Sparsesurv: a Python package for fitting sparse survival models via knowledge distillation.

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.

Results

In this work, we develop a software package and demonstrate how knowledge distillation, a powerful technique in machine learning that aims to transfer knowledge from a complex teacher model to a simpler student model, can be leveraged to learn sparse survival models while mitigati

SUBMITTER: Wissel D 

PROVIDER: S-EPMC11387617 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature