Which technique is used to prevent data leakage in cross-validation, ensuring that information from the test set doesn't influence model training?
A. Leave-One-Out Cross-Validation (LOOCV)
B. Stratified Sampling
C. Holdout Validation
D. Feature Scaling
Answer: Option C
This Question Belongs to Data Science >> Model Evaluation And Validation
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
Join The Discussion