Practice Common Pitfalls in Model Evaluation - 12.4 | 12. Model Evaluation and Validation | Data Science Advance
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

Define overfitting in your own words.

πŸ’‘ Hint: Think about how a student might prepare for an exam.

Question 2

Easy

What is underfitting?

πŸ’‘ Hint: Consider a model that can only draw straight lines.

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 overfitting?

  • A model that performs well on test data
  • A model that is too complex
  • A model that performs poorly on training data

πŸ’‘ Hint: Think about how a student can 'memorize' the material.

Question 2

True or False: Data leakage can make a model appear to have a better performance than it truly does.

  • True
  • False

πŸ’‘ Hint: Consider how improper mixing of datasets can affect results.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Imagine you are developing a model to predict loan approvals. Discuss potential pitfalls considering the imbalanced nature of approvals versus denials.

πŸ’‘ Hint: Consider the ratios of approvals to denials.

Question 2

Design a plan to prevent data leakage during a model training process, detailing your steps.

πŸ’‘ Hint: Think about all data operations involving separate training and test datasets.

Challenge and get performance evaluation