Practice Detection Techniques (2.6.1) - Data Wrangling and Feature Engineering

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10 questions

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6 sample questions

Question 1.
What does a box plot allow you to visualize?
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Think about what features are depicted in the box plot.
Question 2.
What is considered an outlier in the Z-score method?
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Recall the relation of Z-scores to the mean.
3Question 3.
What do box plots effectively show?
- Mean and mode
- Distribution and outliers
- Standard deviation
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Think of the key features of a box plot.
4Question 4.
True or False: A Z-score of 2 indicates an outlier.
- True
- False
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Recall the significance of Z-scores.
Question 5.
Using a provided dataset, create a function to detect outliers with Z-scores and IQR methods. Explain the findings.
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Utilize libraries like NumPy or Pandas for calculations.
Question 6.
Analyze a dataset where some features have different distributions. Discuss how you would choose between Z-score and Isolation Forest methods.
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Think about the shortcomings of each method.

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