Question 2Which statement about K-fold cross-validation below is TRUE?1 pointEach subsample in K-fold cross-validation has at least k observations.Each of the k subsamples in K-fold cross-validation is used as a training set.Each of the k subsamples in K-fold cross-validation is used as a test set.None of the above
Question
Question 2Which statement about K-fold cross-validation below is TRUE?1 pointEach subsample in K-fold cross-validation has at least k observations.Each of the k subsamples in K-fold cross-validation is used as a training set.Each of the k subsamples in K-fold cross-validation is used as a test set.None of the above
Solution 1
The correct statement about K-fold cross-validation is: "Each of the k subsamples in K-fold cross-validation is used as a test set."
Here's why:
In K-fold cross-validation, the original sample is randomly partitioned into k equal sized subsamples. Of the k subsamples, a single subsample is retained as the validation data for testing the model, and the remaining k − 1 subsamples are used as training data. The cross-validation process is then repeated k times, with each of the k subsamples used exactly once as the validation data. The k results can then be averaged to produce a single estimation.
So, each of the k subsamples in K-fold cross-validation is used as a test set once.
Solution 2
The correct statement about K-fold cross-validation is: "Each of the k subsamples in K-fold cross-validation is used as a test set."
Here's why:
In K-fold cross-validation, the original sample is randomly partitioned into k equal sized subsamples. Of the k subsamples, a single subsample is retained as the validation data for testing the model, and the remaining k − 1 subsamples are used as training data. The cross-validation process is then repeated k times, with each of the k subsamples used exactly once as the validation data. The k results can then be averaged to produce a single estimation.
So, each of the k subsamples in K-fold cross-validation is used as a test set once, which makes this statement true.
Similar Questions
In K-Fold Cross-Validation, what does 'K' stand for?Review LaterThe number of times the entire procedure is repeatedThe number of folds the data is split intoThe number of parameters in the modelThe number of validation sets used
Question 8Which of the following statements about cross-validation is/are True?1 pointCross-validation is essential step in hyperparameter tuning.We can manually generate folds by using KFold function.GridSearchCV is commontly used in cross-validation.All of the above are True.
What is the purpose of the k-fold cross-validation technique in machine learning?a.To evaluate a model's performance on a separate test dataset.b.To reduce the risk of overfitting by training and testing a model on different data subsets.c.To speed up the training process by using parallel computing.d.To partition the dataset into k equal subsets for training and testing.
Question 3If a low-complexity model is underfitting during estimation, which of the following is MOST LIKELY true (holding the model constant) about K-fold cross-validation?1 pointK-fold cross-validation will still lead to underfitting, for any k.K-cross-validation with a small k will reduce or eliminate underfitting.K-fold cross-validation with a large k will reduce or eliminate underfitting.None of the above.
What is the purpose of evaluating the model's performance in each iteration of K-Fold Cross-Validation?Review LaterTo determine the optimal hyperparameters for the modelTo measure the model's accuracy on the training dataTo assess how well the model generalizes to unseen dataTo compute the average score for the model
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