Practice Feature Scaling (5.6) - Chapter 5: Data Preprocessing for Machine Learning

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8 questions

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Exercise
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6 sample questions

Question 1.
What is feature scaling?
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Think about adjusting the values of input features.
Question 2.
What is normalization?
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It involves adjusting the values to a certain range.
3Question 3.
What is the main purpose of feature scaling?
- To clean data
- To ensure all features contribute equally
- To split datasets
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Consider what happens when features have different scales.
4Question 4.
True or False: Normalization adjusts values to have a mean of 0.
- True
- False
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Remember what normalization specifically aims for.
Question 5.
A dataset contains ages ranging from 18 to 80 and income ranging from 20000 to 120000. Apply normalization and standardization using Python.
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Don’t forget to fit scalers on the training set before transforming.
Question 6.
Discuss a scenario where normalization might skew results, and why standardization would be a better choice.
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Consider how extreme values influence scaling results.

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