COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
|
Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
|
|
|
True
|
|
|
False
|
|
|
Either A or B
|
|
|
None of the above
|
Detailed explanation-1: -Explanation: For k cross-validation, larger k value implies less bias.
Detailed explanation-2: -A higher k (number of folds) means that each model is trained on a larger training set and tested on a smaller test fold. In theory, this should lead to a lower prediction error as the models see more of the available data.
Detailed explanation-3: -Of course, with cross-validation, the number of folds to use (k-fold cross-validation, right?), the value of k is an important decision. The lower the value, the higher the bias in the error estimates and the less variance.
Detailed explanation-4: -Q22) Which of the following options is/are true for K-fold cross-validation? An increase in K will result in a higher time required to cross-validate the result. Higher values of K will result in higher confidence in the cross-validation result as compared to a lower value of K.
Detailed explanation-5: -For k cross-validation, smaller k value implies less variance.