Outlier Detection & Treatment

Summary

Identifying data points far from the rest — sometimes genuine signal, sometimes an error — and deciding whether to investigate, cap, remove, or leave them, since some models are far more outlier-sensitive than others.

• 1Use a boxplot and the IQR method to flag outliers in a sample dataset.
• 2Learn which model types (linear regression) are outlier-sensitive versus robust (tree-based models).
• 3Practice capping (winsorizing) values as an alternative to deleting them.