Cross-Validation — AWS AI Practitioner (AIF-C01) Practice Questions
Cross-validation is a model evaluation technique that partitions training data into multiple folds, training on subsets and validating on the held-out fold repeatedly to produce a more reliable estimate of generalization performance. AIF-C01 includes cross-validation as part of the broader topic of avoiding overfitting and selecting models that perform well on unseen data. Understanding k-fold cross-validation and when to apply it versus a simple train/validation/test split is relevant to the exam's ML fundamentals domain.
Free questions on cross-validation
What is the purpose of cross-validation in machine learning?
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