Practice Binning (2.3.3) - Data Wrangling and Feature Engineering - Data Science Advance

Practice: Binning

Lesson 2.3.3Data Wrangling and Feature Engineering

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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.
What does binning refer to in data transformation?
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Think about how we categorize things.
Question 2.
Give an example of a binning range for ages.
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Think about how you might group friends by age.
3Question 3.
What is binning?
- Converting categorical data into numeric values
- Grouping continuous numeric data into categorical bins
- Filtering rows from data
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Think about how we might organize data into groups.
4Question 4.
True or False: Binning can improve model interpretability.
- True
- False
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Remember the purpose of organizing data.
Question 5.
You have a dataset of test scores ranging from 0 to 100. Design a binning strategy that would capture the performance into distinct categories that would help a teacher analyze student performance effectively.
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Think about how each category reflects increasing performance.
Question 6.
How might binning skew the analysis of trends in a dataset? Provide a theoretical example.
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Consider how grouping affects visibility in data.

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