I can’t see your gist but spam classification is a textbook example of something you shouldn’t measure with accuracy. If 95% of your samples are not spam you can get 95% accuracy by always guessing not spam.

You should use precision (when your model says “spam” how often is it spam?), recall (how many of the spam emails did it catch), or f1 (balanced between those two).