I’m a data scientist that builds ML models for a living.

You’ve twice now tried to explain how fitting a model works in response to me stating that regression “metrics” are not well suited to describing classification “efficacy”. If anyone is making a basic mistake here it seems to be you failing to understand the difference between a “regression model” like a linear regression, and a “regression metric” like R2.

I’ve stated my semantic meaning of regression vs classification. You can Google this to see it is not a fringe view. It’s been standard for over a decade. Eg https://math.stackexchange.com/questions/141381/what-is-the-...

> Another way to put it is praising the accuracy of a broken clock because it's spot on two times a day.

Accuracy is a classification metric kiddo

Here’s an introductory Wikipedia article if you would like to learn more about what metrics are appropriate for binary classification evaluations:

https://en.m.wikipedia.org/wiki/Evaluation_of_binary_classif...