In cross-validation, what is the main drawback of Leave-One-Out Cross-Validation (LOOCV) compared to k-fold cross-validation?
A. LOOCV requires more computational resources
B. LOOCV is prone to overfitting
C. LOOCV may not be representative of the dataset
D. LOOCV is more computationally efficient
Answer: Option C
Related Questions on Model Evaluation and Validation
What is the primary purpose of a validation dataset in machine learning?
A. To train the model
B. To evaluate the model on unseen data
C. To test the model's performance on training data
D. To visualize data relationships
A. Accuracy
B. Precision
C. Recall
D. F1 Score
A. It reduces the risk of overfitting
B. It reduces the number of folds used in training
C. It increases the model's complexity
D. It decreases the training time
A. Leave-One-Out Cross-Validation (LOOCV)
B. Stratified Sampling
C. Holdout Validation
D. Feature Scaling

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