Given a classifier (SVM) classifying in 2 'classes' (+1 or -1) for prediction purposes. It has an AUC score of 0.28, meaning its success rate is lower than just random predictions.
If I just do the opposite (ie: classifier says it'll be -1, so I'll assume it'll be +1 instead), does that mean my success rate in predicting will be about 72% (1-0.28)?
That doesn't seem very logical to me. Please explain to me how I should interpret this instead and why I can't just do the opposite of the classifier's predictions to get a higher success rate.