We're glad you stopped by involves running another validation study to ensure that results are reproducible A Utility analysis B Meta analysis C Cross validation. This page is here to walk you through essential details with clear and straightforward explanations. Our goal is to make your learning experience easy, enriching, and enjoyable. Start exploring and find the information you need!
Answer :
Cross-validation is a method used to evaluate the performance and generalizability of a predictive model.
It involves dividing the available data into subsets, training the model on some subsets, and then validating it on the remaining subset. By repeating this process multiple times with different subsets, cross-validation helps assess the model's ability to generalize to unseen data and provides an estimate of its performance. It is a crucial step in ensuring that the results obtained from a model are not due to chance or specific to a particular dataset. Cross-validation helps validate and reproduce the findings, enhancing the reliability and robustness of the results.
Full question: _________ involves running another validation study to ensure that results are reproducible.
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