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4 cards from this section. Quick, good the night before a test.
What are outliers?
Think about extreme values in data.
Give one method to handle outliers.
What can we do with data points that are much higher or lower?
What is an outlier?
Think about what makes a data point unusual.
True or False: Removing outliers is always the best method of handling them.
Consider the implications of losing data.
A dataset containing house prices shows a few extremely high values due to luxury homes. Discuss how you would address these outliers and justify your approach.
Consider how the outliers influence the overall predictions.
If a linear regression model's results skew due to an outlier, what steps can you take during preprocessing to ensure better outcomes? Provide specific transformation methods you would apply.
Think about how we rescale data to handle skew.
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