I have 5 classes to predict. When I try to predict all of them using one model it doesn’t work that well so I decided to stack 5 binary classification models.
Say I want predict class 1 and I have a folder with all class 1 images and another folder with classes 0,2,3,4.
After training, the output of my model looks like:
class 0: 51.39% | class 1: 81.57%
My question is the following, does the ability of the model to predict class 0 (composed of 0234) is important here?
I believe it would be beneficial for the model to see other classes but I can’t wrap my head around why it would be important to have 100% accuracy for both classes.