scikit-survival ...
Zenodo, 2023
academicJournal
Zugriff:
This is a major release bringing new features and performance improvements. sksurv.nonparametric.kaplan_meier_estimator() can estimate pointwise confidence intervals by specifying the conf_type parameter. sksurv.ensemble.GradientBoostingSurvivalAnalysis supports early-stopping via the monitor parameter of sksurv.ensemble.GradientBoostingSurvivalAnalysis.fit(). sksurv.metrics.concordance_index_censored() has a significantly reduced memory footprint. Memory usage now scales linear, instead of quadratic, in the number of samples. Fitting of sksurv.tree.SurvivalTree, sksurv.ensemble.RandomSurvivalForest, or sksurv.ensemble.ExtraSurvivalTrees is about 3x faster. Finally, the release adds support for Python 3.11 and pandas 2.0. Bug fixes Fix bug where times passed to sksurv.metrics.brier_score() was downcast, resulting in a loss of precision that may lead to duplicate time points (#349). Fix inconsistent behavior of evaluating functions returned by predict_cumulative_hazard_function or predict_survival_function ...
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scikit-survival ...
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Autor/in / Beteiligte Person: | Pölsterl, Sebastian |
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Veröffentlichung: | Zenodo, 2023 |
Medientyp: | academicJournal |
DOI: | 10.5281/zenodo.8025038 |
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