I trained a classification model on some data with two classes and have really low accuracy. I have a false-positive rate of 86 % for both classes I am trying to predict. I was wondering if I could get a good Classifier in the following way (described in pseudocode, where x is a data point and Classifier(x) is my bad classifier applied to x.)
for a point x: if Classifier(x) gives Class 1 return Class 2. else Classifier(x) gives Class 2 return Class 1.
Basically, I would ask a new classifier to choose the opposite of whatever my bad classifier chose. Is this valid? Am I right in thinking that I will get a 86 % true positive rate for both classes? I would love any intuition on the creation of such a classifier.
Thanks in advance.