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Therefore, we needed to introduce a routine to select the best-performing machine learning model by doing the following: (1) using resampling techniques such as cross-validation, (2) reducing the variance of cross-validation, and (3) performing feature selection on all runs to prevent overfitting.
Standard k-fold cross-validation divides a data set into k nonoverlapping subsets. Each model is trained on k–1 folds and evaluated on the k-th fold, for a total of k models.
JMIR Aging 2022;5(1):e28333
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