In k-fold cross-validation, if you choose a higher value of k (e.g., k = 10), what effect does it have on the model evaluation process?
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
Answer: Option A
This Question Belongs to Data Science >> 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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