scikit-survival 0.23.0 released

I am pleased to announce the release of scikit-survival 0.23.0.

This release adds support for scikit-learn 1.4 and 1.5, which includes missing value support for RandomSurvivalForest. For more details on missing values support, see the section in the release announcement for 0.23.0.

Moreover, this release fixes critical bugs. When fitting SurvivalTree, the sample_weight is now correctly considered when computing the log-rank statistic for each split. This change also affects RandomSurvivalForest and ExtraSurvivalTrees which pass sample_weight to the individual trees in the ensemble. Therefore, the outputs produced by SurvivalTree, RandomSurvivalForest, and ExtraSurvivalTrees will differ from previous releases.

This release fixes a bug in ComponentwiseGradientBoostingSurvivalAnalysis and GradientBoostingSurvivalAnalysis when dropout is used. Previously, dropout was only applied starting with the third iteration, now dropout is applied in the second iteration too.

Finally, this release adds compatibility with numpy 2.0 and drops support for Python 3.8.


scikit-survival is available for Linux, macOS, and Windows and can be installed either

via pip:

pip install scikit-survival

or via conda

 conda install -c conda-forge scikit-survival
Sebastian Pölsterl
AI Researcher

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