Practice Data Leakage (12.4.C) - Model Evaluation and Validation - Data Science Advance

Practice: Data Leakage

Lesson 12.4.CModel Evaluation and Validation

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8 questions

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2 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
2 questions · about 1 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is data leakage?
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Think about how test data might influence training performance.
Question 2.
Name one common cause of data leakage.
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Consider preprocessing steps.
3Question 3.
What is data leakage?
- A method of scaling data
- Using the test data for training
- Improving model accuracy
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Consider the impact of using test data in the training set.
4Question 4.
True or False: Data leakage can result in a model that works well on training data but poorly in actual application.
- True
- False
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Think about the difference between training and real-world scenarios.
Question 5.
Suppose you have processed a dataset where features were scaled using the entire dataset before splitting. Discuss how this can affect the model's performance during real-world application.
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Think about the implications when the model encounters new data.
Question 6.
Construct a data processing pipeline that incorporates checks for data leakage. Discuss how each step can mitigate potential leakage issues.
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Consider the sequence and relationships between each step in data processing.

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