Practice Label Encoding (2.3.5) - Data Wrangling and Feature Engineering

Practice: Label Encoding

Lesson 2.3.5Data Wrangling and Feature Engineering

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9 questions

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6 sample questions

Question 1.
What is label encoding?
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Think about how machines understand data.
Question 2.
Is label encoding suitable for nominal data?
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Consider what happens with ordered categories.
3Question 3.
What does label encoding do?
- Converts categorical variables to audio signals
- Converts categorical variables to numeric values
- Excludes categorical variables from analysis
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Think about the role of data in machine learning.
4Question 4.
Is label encoding suitable for nominal data?
- True
- False
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Remember the difference between ordinal and nominal.
Question 5.
You have a dataset containing categorical variable 'Education' with values 'High School', 'Bachelor', and 'Master'. Discuss how you would apply label encoding here and explain the reasoning behind your approach.
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Consider the implications of the educational levels on future processes.
Question 6.
Suppose you're given a dataset with a nominal variable 'Color' that includes 'Red', 'Blue', and 'Green'. What would be the challenges of applying label encoding here, and how could that affect your model?
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Think about how categories relate (or don’t relate) to each other.

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