Practice Best Practices - 12.7 | 12. Model Evaluation and Validation | Data Science Advance
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12.7 - Best Practices

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is the purpose of a held-out test set?

💡 Hint: Think about why we should avoid using training data for tests.

Question 2

Easy

Why is cross-validation useful?

💡 Hint: Consider how multiple tests can lead to a better average performance estimate.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

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

💡 Hint: Think about how many samples are involved in the evaluation process.

Question 2

True or False: Monitoring overfitting is unnecessary if you're using cross-validation.

  • True
  • False

💡 Hint: Consider whether cross-validation completely prevents overfitting.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You are building a predictive model for customer churn in a subscription service. How would you document the model evaluation process to ensure reproducibility?

💡 Hint: Consider all factors that contribute to the final results.

Question 2

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.

💡 Hint: Evaluate how different practices can help fix the problem.

Challenge and get performance evaluation