Practice Normalization (1.2.2) - Chapter 6: AI and Machine Learning in IoT

Practice: Normalization

Lesson 1.2.2AI and Machine Learning in IoT

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10 questions

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4 cards from this section. Quick, good the night before a test.
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Quiz
4 questions · about 3 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is normalization in the context of machine learning?
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Think about why having different scales in features could be problematic.
Question 2.
Name one method of normalization.
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Remember 'Min-Max' scales data between 0 and 1.
3Question 3.
What is the primary purpose of normalization in machine learning?
- To remove data
- To adjust scales of features
- To enhance data quality
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Think about how different scales can affect model behavior.
4Question 4.
True or False: Normalization helps models to process data consistently regardless of original value ranges.
- True
- False
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Reflect on the impact of varying scales.
Question 5.
Propose a comprehensive workflow for normalizing a dataset with multiple features, including outliers. Describe each step clearly.
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Think sequentially about how to process with outliers included.
Question 6.
Given a hypothetical scenario with fluctuating readings from IoT sensors due to environmental changes, how would you adapt your normalization methods over time?
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Consider what practices could maintain accuracy over time.

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