The article explores two primary resampling methods in machine learning: k-fold cross-validation and bootstrapping, explaining their implementations, advantages, and use cases. It details how these me
thods help assess model generalization performance, with k-fold CV offering unbiased but more variable results, while bootstrapping provides less variable but potentially biased estimates.
Reasons to Read -- Learn:
how to properly validate machine learning models using different resampling techniques, with practical implementations using tools like h2o and rsample packages
specific advantages and tradeoffs between k-fold cross-validation and bootstrapping, including when to use each method based on your dataset size (n < 10,000 vs n ≥ 1,000)
practical guidelines for choosing resampling parameters, such as selecting the optimal number of folds (k=5 or k=10) and understanding that bootstrapping captures approximately 63.21% of original samples
6 min readauthor: A.I Hub
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