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A general bootstrap performance diagnostic
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '13
As datasets become larger, more complex, and more available to diverse groups of analysts, it would be quite useful to be able to automatically and generically assess the quality of estimates, much as we are able to automatically train and evaluate predictive models such as classifiers. However, despite the fundamental importance of estimator quality assessment in data analysis, this task has eluded highly automatic solutions. While the bootstrap provides perhaps the most promising step in thisdoi:10.1145/2487575.2487650 dblp:conf/kdd/KleinerTASJ13 fatcat:wyrwmiy6dnffzobot446ifxugy