> But not so fast. B, for instance, might be correlated with A. In this case, the coefficients are also correlated and interpretability becomes much more nuance

> But not so fast. B, for instance, might be correlated with A. In this case, the coefficients are also correlated and interpretability becomes much more nuanced. And this isn’t the exception, it’s the rule. If you have a lot of features, chances are many of them are correlated. Aren't we supposed to remove correlated predictors from models? Or we can use techniques like PCA? But then, once again, we fall into the realm of unexplainable.