Practice Data Preprocessing For Machine Learning (5) - Chapter 5: Data Preprocessing for Machine Learning

Practice: Data Preprocessing for Machine Learning

Lesson 5Data Preprocessing for Machine Learning

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10 questions

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5 cards from this section. Quick, good the night before a test.
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Quiz
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6 sample questions

Question 1.
What does data preprocessing involve?
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Think about what happens to data before it's inputted into a model.
Question 2.
Why is handling missing data important?
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Remember the impact of 'Garbage in, garbage out.'
3Question 3.
Which of the following is NOT a reason for data preprocessing?
- Remove noise
- Encode categorical data
- Increase model complexity
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Focus on the purpose of preprocessing.
4Question 4.
True or False: Missing values can be left unhandled in a dataset for a machine learning algorithm.
- True
- False
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Think about the implications of having missing values.
Question 5.
Given a dataset with numerous NaNs, propose a comprehensive strategy to handle the missing values, detailing your steps and rationale.
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Consider both the volume and the significance of the missing data when deciding how to handle it.
Question 6.
You have a dataset where 'Country' has high cardinality. Discuss the trade-offs of using OneHotEncoder versus Label Encoding for preprocessing.
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Think about how the algorithm perceives numerical relationships.

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