In k-fold cross-validation, what happens in each iteration?

Question

BlackTom AI · Accepted Answer

In k-fold cross-validation, the dataset is partitioned into k equal (or near-equal) sized folds, and the model undergoes k training/testing rounds. 
Option 1: 'One fold is used for validation, the other folds for training' describes exactly this process, where in each iteration one fold serves as the validation set and the remaining k-1 folds form the training set. This allows the model to be evaluated on data it hasn't seen during training while being trained on the rest. 
Option 2: 'The model is tested on the full dataset' is inaccurate because testing on the full dataset would not provide a meaningful assessment of generalization, since the model would have been trained on part of that data. 
Option 3: 'Data is randomly generated' is incorrect; k-fold cross-validation uses partitions of the existing data, not new or randomly generated data, to create training and validation splits. 
Option 4: 'All folds are combined at once' misstates the procedure; while predictions from each fold can be aggregated after all iterations, the core process requires evaluating the model on one held-out fold at a time, not simultaneously on all folds during a single iteration.

COMM_V 414 103 2025W1 Concept Check 3 (individual)

In k-fold cross-validation, what happens in each iteration?

View Explanation

Log in for full answers

Similar Questions

Why was model-building strategy #1 used to estimate generalization performance on the income data for the decision tree, logistic regression, and random forest models?

What is one advantage of k-fold cross-validation over a single holdout set?

Which of the following is TRUE about cross validation?

Question at position 1 True/False Question: k-fold cross-validation is used to find which fold of the data set gives the best model. TrueFalse

Which is not a benefit of using cross-validation for model evaluation?

Why does the code 4 notebook use cross-validation when selecting the alpha value for regularization?

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