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- 1. Multiple Choice
Which of the following is not used to reduce overfitting?
Stopping the training earlier
Using a larger dataset
Reducing the complexity of the model
Using k-fold cross validation
- 2. Multiple Choice
Which of the following will certainly reduce the size of the confidence interval for a model's error rate?
Increasing the number of examples in your sample
Improving your model to reduce its error rate
- 3. Multiple Choice
Which of our data sets should we use to calculate the sample error of our model?
Train
Dev
Test
- 4. Multiple Choice
In the special case where our model sample error is zero, which of the following is true when using the sample error formula:
The confidence interval will depend on the sample size
The confidence interval will be the same regardless of the sample size
- 5. Multiple Choice
Which of the following is not a solution for dealing with an imbalanced dataset?
Downsample the majority class
Use several metrics, choosing the one that reflects the intended model behaviour
Use k-fold cross validation
- 6. Multiple Choice
Which of the following statements is false?
Overfitted models perform better on the training data than on the test data
Overfitting can occur when learning is performed for too long
Overfitting can occur if the training set is not representative
Underfitted models always generalise well to different datasets
- 7. Multiple Choice
What is a common method to handle missing data in a dataset?
Remove all rows with missing values
Use a different model
Ignore the missing data
- 8. Multiple Choice
Which of the following is a technique to prevent overfitting?
Adding more features to the model
Using dropout in neural networks
Increasing the learning rate
- 9. Multiple Choice
What is the purpose of using a validation set in model training?
To train the model
To test the model's performance on unseen data
To tune hyperparameters
- 10. Multiple Choice
What is the purpose of using cross-validation in model evaluation?
To increase the model's accuracy
To assess the model's performance on different subsets of the data
To reduce the model's training time
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