Euclidean Distance vs. Mahalanobis Distance

Euclidean distance fails to account for data distribution and feature correlation, making it unreliable for tasks like outlier detection. Mahalanobis distance solves this by transforming data into uncorrelated variables with unit variance before computing distance, effectively considering the underlying distribution. The post also covers cost-complexity pruning (CCP) in decision trees, which prevents overfitting by balancing classification cost against tree complexity, and explains how bagging reduces variance through bootstrap sampling and model aggregation.