# How to make really bad results from a machine learning model better by reversing predictions

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.