SHAP is worth knowing about for reasons other than its red and blue stripes

SHAP is worth knowing not because its visualizations are easily recognizable by their red and blue stripes. What’s more important is this: behind the familiar graph lies a method that various research groups have continued to develop since its release in 2017.

This article explores SHAP not through its code, but through its origins and evolution. This is a good approach for those who use machine learning models but want to understand where the explanations for their decisions come from, rather than simply inserting a ready-made graph into a report.

The main point of interest is that SHAP has not remained a static framework. New extensions have emerged around the original idea, and the method now has a notable development history. The article compiles exactly 15 such extensions—not as a random list of features, but as an answer to the question of why the tool didn’t disappear after the initial wave of interest.

There’s also an editorial twist: the headline promises 16 reasons plus one more, even though the description explicitly states 15 extensions. The explanation is hidden within the article itself, so summarizing it without reading the text means missing part of the point.

The article’s limitation is clear: it contains no code and isn’t a practical guide to implementing SHAP. Don’t look here for accuracy comparisons, benchmarks, or a ready-made recipe for a specific model. However, it’s a good starting point if you first need to understand the concept and scope of the tool.

If you use model explanations, is it more important for you to understand the mechanics of SHAP itself, or to be able to quickly generate a nice visualization? Source: All Articles / Machine Learning / Habr