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Bootstrapping refits a model or recomputes a statistic on many resamples of the data drawn with replacement, and the spread of the results is a confidence interval that needs no formula.
Bootstrapping estimates the distribution of an estimator by resampling the data, assigning measures of accuracy to sample estimates (Wikipedia). It gives confidence intervals for a C-index or AUC on a small test set, and, applied to one patient, the spread of predictions across bootstrap-refitted models is a per-prediction reliability signal: a prediction whose tenth-to-ninetieth percentile range is wider than an in-domain cutoff should be flagged rather than reported.
Shares Out-of-distribution detection (Mahalanobis guard), Uncertainty quantification and confidence gates, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Permutation test and the Mann-Whitney U test, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Permutation test and the Mann-Whitney U test, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Cross-validation and stratified k-fold, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Uncertainty quantification and confidence gates, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Out-of-distribution detection (Mahalanobis guard), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Cross-validation and stratified k-fold, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Out-of-distribution detection (Mahalanobis guard), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.