
TL;DR
This paper emphasizes the importance of prospective validation for predictive models, highlighting its role in realistic assessment and reproducibility, which is often overlooked by retrospective testing.
Contribution
It introduces the concept of prospective validation as a crucial approach for meaningful model evaluation in real-world settings.
Findings
Prospective validation improves model assessment accuracy.
Incorporating trained models and subjective decisions enhances reproducibility.
Prospective experiments are vital for consistent progress in modeling.
Abstract
Retrospective testing of predictive models does not consider the real-world context in which models are deployed. Prospective validation, on the other hand, enables meaningful comparisons between data generation processes by incorporating trained models and considering the subjective decisions that affect reproducibility. Prospective experiments are essential for consistent progress in modeling.
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