Practice Best Practices (12.7) - Model Evaluation and Validation - Data Science Advance

Practice: Best Practices

Lesson 12.7Model Evaluation and Validation

Practise this section

10 questions

Flashcard drill
5 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
Sign up to start the exercise
Quiz
4 questions · about 3 min · pass mark 70%. Hints available.
Sign up to start the quiz

6 sample questions

Question 1.
What is the purpose of a held-out test set?
Show hintHide hint
Think about why we should avoid using training data for tests.
Question 2.
Why is cross-validation useful?
Show hintHide hint
Consider how multiple tests can lead to a better average performance estimate.
3Question 3.
What is the purpose of cross-validation in model evaluation?
- To make training faster
- To improve performance on training data
- To provide a stable performance estimate
Show hintHide hint
Think about how many samples are involved in the evaluation process.
4Question 4.
True or False: Monitoring overfitting is unnecessary if you're using cross-validation.
- True
- False
Show hintHide hint
Consider whether cross-validation completely prevents overfitting.
Question 5.
You are building a predictive model for customer churn in a subscription service. How would you document the model evaluation process to ensure reproducibility?
Show hintHide hint
Consider all factors that contribute to the final results.
Question 6.
Imagine you have a predictive model that is showing high accuracy on training data but low on validation data. List potential reasons for this and how you would address them.
Show hintHide hint
Evaluate how different practices can help fix the problem.

6 more questions in this section. Enroll free to practise them all.