Can NaN class be assigned to a certain class in imbalanced datasets (binary classification)? I'm working on a spam detection binary classification problem, but the dataset is very imbalanced (99% to 1%). I know there are techniques like over/under sampling, but I don't think it can be used in this scenario due to the extremely low raw number of positive class samples.
However, a large number of the samples in the dataset have a "NaN" class. I was wondering if it is sensible to assign these classes as belonging to the positive class, or if this is something you should never do in a binary classification problem (in case it's considered as "cheating").
I'm planning on using common algorithms such as logistic regression, KNN, SVM, etc. Perhaps instead of reassigning NaN samples, there is another way of modelling which is better suited to this problem?
Thanks

No, you can't do that. If you are missing the labels, you don't know them, they can be either positive or negative. If you made the labels positive you are possibly adding incorrect labels to the data, so adding noise to it, biasing your results.
