Practice Common Pitfalls In Model Evaluation (12.4) - Model Evaluation and Validation

Practice: Common Pitfalls in Model Evaluation

Lesson 12.4Model Evaluation and Validation

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9 questions

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4 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
Define overfitting in your own words.
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Think about how a student might prepare for an exam.
Question 2.
What is underfitting?
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Consider a model that can only draw straight lines.
3Question 3.
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
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Think about how a student can 'memorize' the material.
4Question 4.
True or False: Data leakage can make a model appear to have a better performance than it truly does.
- True
- False
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Consider how improper mixing of datasets can affect results.
Question 5.
Imagine you are developing a model to predict loan approvals. Discuss potential pitfalls considering the imbalanced nature of approvals versus denials.
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Consider the ratios of approvals to denials.
Question 6.
Design a plan to prevent data leakage during a model training process, detailing your steps.
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Think about all data operations involving separate training and test datasets.

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