scikit-survival

This release adds support for scikit-learn 1.0, which includes support for feature names. If you pass a pandas dataframe to fit, the estimator will set a feature_names_in_ attribute containing the feature names. When a dataframe is passed to predict, it is checked that the column names are consistent with those passed to fit. See the scikit-learn release highlights for details. Bug fixes Fix a variety of build problems with LLVM (#243). Enhancements Add support for feature_names_in_ and n_features_in_ to all estimators and transforms. Add sksurv.preprocessing.OneHotEncoder.get_feature_names_out. Update bundeled version of Eigen to 3.3.9. Backwards incompatible changes Drop min_impurity_split parameter from sksurv.ensemble.GradientBoostingSurvivalAnalysis. base_estimators and meta_estimator attributes of sksurv.meta.Stacking do not contain fitted models anymore, use estimators_ and final_estimator_, respectively. Deprecations The normalize parameter of sksurv.linear_model.IPCRidge is deprecated and will be removed in a future version. Instead, use a sciki-learn pipeline: make_pipeline(StandardScaler(with_mean=False), IPCRidge())