Machine Learning Intermediate Quiz 5

1. Which of these is a drawback of k-Nearest Neighbors?

2. What is the main goal of model regularization?

3. Which of these is a common loss function for classification?

4. What is the main difference between L1 and L2 regularization?

5. Which method is used for hyperparameter tuning?

6. What does "stratified sampling" ensure?

7. Which of these is a limitation of decision trees?

8. What is the main purpose of using pipelines in scikit-learn?

9. Why is feature scaling important for distance-based algorithms like k-NN?

10. What is a validation curve typically used to analyze?