Practice Feature Scaling (5.8) - Data Cleaning and Preprocessing - Data Science Basic

Practice: Feature Scaling

Lesson 5.8Data Cleaning and Preprocessing

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9 questions

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3 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 is normalization?
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Think of how values are limited to a specific range.
Question 2.
What is the primary goal of standardization?
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Consider the shape of a normal distribution.
3Question 3.
What is the purpose of normalization in feature scaling?
- To reduce variance
- To bring all features to a common scale
- To eliminate missing values
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Think about how different features are represented numerically.
4Question 4.
True or False: Standardization rescales data based on the maximum feature value.
- True
- False
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Remember how data is centered in statistical terms.
Question 5.
You have a dataset with 'Height in cm' and 'Weight in kg'. How would you decide whether to normalize or standardize this data before using it in a machine learning model?
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Consider ranges and distributions of the features.
Question 6.
Create a small dataset containing two features: Age and Salary. Demonstrate how to apply both normalization and standardization using code, explaining your steps.
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Ensure to use respective scaling libraries.

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