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Question 1 of 8
To increase the size of the dataset
To speed up model training
To assess model performance on unseen data
To reduce the need for feature engineering
Question 2 of 8
By increasing the model's complexity
By using all data for training
By testing the model on multiple subsets of data
By reducing the number of features
Question 3 of 8
When you have an unlimited amount of data
When you need to make quick, one-time predictions
When you have a small dataset and need to maximize its use
When you're only interested in the model's performance on training data
Question 4 of 8
Which of the following is NOT a typical application of cross-validation?
Evaluating a disease prediction model in healthcare
Testing a stock market prediction model in finance
Improving a recommendation system in e-commerce
Determining the optimal learning rate for a neural network
Question 5 of 8
Which cross-validation technique is most appropriate for an imbalanced dataset?
Hold-Out Cross-Validation
K-Folds Cross-Validation
Stratified K-Folds Cross-Validation
Leave-One-Out Cross-Validation
Question 6 of 8
When dealing with time series data, which cross-validation technique should be used?
Time Series Cross-Validation
Nested K-Folds Cross-Validation
Leave-P-Out Cross-Validation
Question 7 of 8
Which cross-validation technique is most computationally intensive for large datasets?
Repeated K-Folds Cross-Validation
Question 8 of 8
What is the primary purpose of Nested K-Folds Cross-Validation?
To handle imbalanced datasets
To evaluate time series models
For hyperparameter tuning
To reduce computational cost