Abstract
In order to make sure that machine learning models are reliable and broadly applicable, cross-validation approaches are essential. They offer a methodical approach for adjusting hyperparameters, assessing model performance, and resolving issues with overfitting, unbalanced data, and temporal dependencies. This review article provides a thorough analysis of the many cross-validation strategies used in machine learning, from conventional techniques like k-fold cross-validation to more specialized strategies for particular kinds of data and learning objectives. In addition to current developments and best practices in cross-validation methodology, we go over the fundamentals, uses, benefits, and drawbacks of each technique. We also highlight important factors to take into account and recommendations for choosing suitable cross-validation procedures based on the properties of the dataset and the modelling goals. The objective of this study is to give academics and practitioners a thorough grasp of cross-validation approaches and their significance in developing robust and dependable machine learning models by synthesizing the available literature.
Published Version
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