Statistical Distances for ML

This post introduces the concept and application of robust statistical distances for machine learning, particularly for outlier and anomaly detection. It details various distance metrics like Kolmogorov-Smirnov, Earth Mover's, and Cramér-von Mises, and provides interactive visualizations to compare different probability distributions. The implementation uses D3.js for visualization and demonstrates how these distances can be used to build more powerful ML algorithms.