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?

View Explanation

Log in for full answers

Similar Questions

In a 75%/25% Cross-Validation

Please select the incorrect statements about k-fold cross-validation and Leave-One-Out Cross-Validation (LOOCV):

Assuming doing a 10-fold cross validation for linear regression (Y = AX + B). It is possible that 10 different As and Bs would be generated, one for each model learned.

Cross-validation is a special case of the validation set approach.

Which of the following approaches is best when deciding what model to use, e.g. when deciding what polynomial degree to use in regression?

In a consumer society, many adults channel creativity into buying things

Economic stress and unpredictable times have resulted in a booming industry for self-help products

People born without creativity never can develop it

More Practical Tools for Students Powered by AI Study Helper

Homework AI Solver

Stylized AI Paper Writer

Plagiarism Checker Assistant

Citation AI Academic Writing Tool

In-Class Translation Assistant

AI Note Generator

AI Quiz Answers

Past Exam Questions from University Test Bank

Smart Practice Assistant

Adaptive Practice

Making Your Study Simpler