scikit-survival 0.4 released and presented at PyCon UK 2017

I’m pleased to announce that scikit-survival version 0.4 has been released.

This release adds CoxnetSurvivalAnalysis, which implements an efficient algorithm to fit Cox’s proportional hazards model with LASSO, ridge, and elastic net penalty. This allows fitting a Cox model to high-dimensional data and perform feature selection. Moreover, it includes support for Windows with Python 3.5 and later by making the cvxopt package optional.


You can install the latest version via Anaconda (OSX and Linux):

 conda install scikit-survival 

or via pip (all platforms):

 pip install -U scikit-survival 

PyCon UK

Last week, I presented an Introduction to Survival Analysis with scikit-survival at PyCon UK in Cardiff in front of a packed audience of genuinely interested people. I hope some people will give scikit-survial a try and use it in their work.

The slides of my presentation are available at

Sebastian Pölsterl
AI Researcher

My research interests include machine learning for time-to-event analysis, causal inference and biomedical applications.