Practice Standardization (z-score Scaling) (5.8.2) - Data Cleaning and Preprocessing

Practice: Standardization (Z-score Scaling)

Lesson 5.8.2Data 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
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6 sample questions

Question 1.
What is the formula for calculating the Z-score?
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Remember to consider how both mean and standard deviation are involved.
Question 2.
Why is standardization important?
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Think about different scales of data.
3Question 3.
What does standardization do to a dataset?
- Convert all data to a common scale
- Remove outliers
- Change categorical data to numerical
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Think about the concept of mean and deviation.
4Question 4.
True or False: Standardization is necessary for all types of data.
- True
- False
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Consider cases where features are homogeneous.
Question 5.
Suppose you have a dataset with weight, height, and age of individuals. Explain how you would prepare this dataset for a clustering algorithm using Z-score scaling.
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Consider how different measurements affect clustering.
Question 6.
You are given a choice between normalization and standardization for a dataset heavily skewed by outliers. Which method would you choose and why?
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Recall the effect of outliers on each method.

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