Feature attribution: the art of assigning blame to the right feature. It's like pointing fingers, but with more math.
Imagine you have a neural network, and it's like a big ol' mess. Some features are doing great, but others are just standing there, looking pretty. That's where feature attribution comes in - to figure out which ones are the real culprits.
It's all about identifying the most important features, like finding the needle in the haystack. Or in our case, the feature that's making our neural network's predictions go haywire.
So, how do we do it? We use some fancy algorithms, like SHAP or LIME. They're like feature attribution detectives, sniffing out the bad apples.
Want to learn more about SHAP? SHAP: The Feature Attribution Detective
Or, if you're feeling adventurous, check out our LIME guide for a different take on feature attribution.
Feature Attribution = SHAP / LIME / Other Fancy Algorithm