scikit-survival 0.14 with Improved Documentation Released

Today marks the release of version 0.14.0 of scikit-survival. The biggest change in this release is actually not in the code, but in the documentation. This release features a complete overhaul of the documentation. Most importantly, the documentation has a more modern feel to it, thanks to the visually pleasing pydata Sphinx theme, which also powers pandas.

Moreover, the documentation now contains a User Guide section that bundles several topics surrounding the use of scikit-survival. Some of these were available as separate Jupyter notebooks previously, such as the guide on Evaluating Survival Models. There are two new guides: The first one is on penalized Cox models. It provides a hands-on introduction to Cox’s proportional hazards model with $\ell_2$ (Ridge) and $\ell_1$ (LASSO) penalty. The second guide, is on Gradient Boosted Models and covers how gradient boosting can be used to obtain a non-linear proportional hazards model or a non-linear accelerated failure time model by using regression tree base learners. The second part of this guide covers a variant of gradient boosting that is most suitable for high-dimensional data and is based on component-wise least squares base learners.

To make it easier to get started, all notebooks can now be run in a Jupyter notebook, right from your browser, just by clicking on

In addition to the vastly improved documentation, this release includes important bug fixes. It fixes several bugs in CoxnetSurvivalAnalysis, where predict, predict_survival_function, and predict_cumulative_hazard_function returned wrong values if features of the training data were not centered. Moreover, the score function of ComponentwiseGradientBoostingSurvivalAnalysis and GradientBoostingSurvivalAnalysis will now correctly compute the concordance index if loss='ipcwls' or loss='squared'.

For a full list of changes in scikit-survival 0.14.0, please see the release notes.

Pre-built conda packages are available for Linux, macOS, and Windows via

 conda install -c sebp scikit-survival

Alternatively, scikit-survival can be installed from source following these instructions.

Sebastian Pölsterl
Post-Doctoral Researcher

My research interests include machine learning for time-to-event analysis, non-Euclidean data, and biomedical applications.