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.
Install
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