Practice Encoding Categorical Data (5.4) - Chapter 5: Data Preprocessing for Machine Learning

Practice: Encoding Categorical Data

Lesson 5.4Data Preprocessing for Machine Learning

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9 questions

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

Question 1.
What is the main purpose of encoding categorical data?
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Think about what type of data machine learning algorithms work best with.
Question 2.
Can you explain OneHotEncoding?
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Remember how categories are transformed into separate columns.
3Question 3.
What is OneHotEncoding used for?
- To convert text to integers
- To create binary columns for categories
- To scale numerical values
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Think about how categories are represented in a dataset.
4Question 4.
True or False: LabelEncoding is always the best choice for categorical variables.
- True
- False
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Consider the nature of the data you are encoding.
Question 5.
Suppose you have a dataset containing countries and a product rating ('Good', 'Average', 'Bad'). Outline an approach for encoding this dataset to prepare it for a machine learning model.
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Consider the nature of the ratings and how they should influence model training.
Question 6.
Given a dataset of survey responses including 'Yes', 'Sometimes', 'No' as answers, suggest an encoding strategy that maintains the options’ intrinsic order while encoding them for a model.
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Think about how to maintain the order when encoding.

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