Suppose we are going to perform K-fold cross-validation. If we make 5 folds from the data set that has 100 observations. Which of the following are true statements?

Question

BlackTom AI · Accepted Answer

The provided data for this question is incomplete: there are no answer options listed in answer_options to analyze, so I cannot perform option-by-option reasoning as requested.

Nevertheless, I can discuss the two statements mentioned in the prompt and clarify their validity in the context of 5-fold cross-validation with 100 observations.

- Statement A: 'The holdout cross-validation can be performed 5 times.'
In standard terminology, holdout cross-validation refers to a single split of the data into a training and a test set, evaluated once. Repeating a holdout process multiple times is more characteristic of repeated holdout or Monte Carlo cross-validation, not conventional k-fold cross-validation. When we perform 5-fold cross-validation, we typically interpret this as partitioning the data into 5 equal folds and conducting 5 training/testing evaluations, each time using 4 folds for training and 1 fold for testing, rotating which fold is held out. Therefore, calling this a 'holdout cross-validation' performed 5 times is not the standard wording for 5-fold cross-validation, and this statement is generally considered false or at least imprecise in standard terminology.

- Statement B: 'The final error is the average of the error found from each holdout cross-validation.'
In 5-fold cross-validation, after performing the 5 train/test evaluations (one per fold), we typically aggregate the performance by averaging the error (or other metric) across the 5 folds. This description aligns with the standard definition of how the final cross-validation error is computed for k-fold cross-validation. Therefore, this statement is true.

In summary, given typical definitions: the second statement is true, while the first statement is false or at least nonstandard in terminology. If the actual answer options were provided, we would map each option to this interpretation and give targeted reasoning for each one accordingly.

Machine Learning: Fundamentals and Applications Quiz 4 Model Selection

Suppose we are going to perform K-fold cross-validation. If we make 5 folds from the data set that has 100 observations. Which of the following are true statements?

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