1. What is overfitting?
2. Why should we use cross-validation?
3. Why can it be bad if our metrics are improving on the test set? Which features are useful for improving model performance on cross-validation?
4. Why do some features decrease the performance of a decision tree on test data or in cross-validation?
5. What is the difference between the random search and grid search algorithms for parameter optimization?
6. Why is Git not sufficient for data version control?
7. What are the alternatives to DVC for data version control and experimentation logging?