2.6. Dealing with Outliers

Let's start by discussing how we can detect outliers in our datasets. Who can tell me what a box plot is?

Isn't that the graph that shows the median and quartiles of the data?

Exactly! Box plots allow us to visually spot outliers. Now, can anyone tell me about the Z-score method?

The Z-score measures how many standard deviations a data point is from the mean. If it’s greater than 3, it could be an outlier?

Right again! Remember Z-score as 'Zero to three' for potential outliers. What about the IQR method?

IQR looks at the interquartile range, right? Any values that fall outside 1.5 times the IQR from Q1 and Q3 are outliers.

Great! Finally, we also have Isolation Forests. This method uses machine learning to spot anomalies. Let's recap: box plots, Z-scores, IQR, and Isolation Forests are all ways to detect outliers!

Now that we have detected outliers, let’s go over how to handle them. What are some options we have?

We can just remove them from the dataset if they’re too extreme!

That's one option! Capping or flooring outliers is another approach. But can anyone explain what using robust models means?

It means using models that won’t be affected as much by outliers, like tree-based algorithms?

Exactly! And transformations can also help. Why might we use a log transformation?

To compress skewed data, making it easier to analyze.

Excellent! Remember to use your 'PART' strategy: Remove, Cap, Robust models, and Transform. These treatments help ensure our analyses are valid!