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Statistical Distances for ML

Statistical Distances for ML

1
posts
2017

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.

2017

Robust statistical distances for machine learning | Datadog

9/6/2017

This post introduces and explains the implementation of robust statistical distances for machine learning, focusing on their application in outlier and anomaly detection. It includes interactive visualizations using D3.js to compare various probability distributions and calculate metrics such as Kolmogorov-Smirnov Distance, Earth Mover's Distance, and Cramér-von Mises Distance. The post provides code examples for generating samples from different distributions and calculating these distances.