Hello good people.
So, I would want to do predictions using image data of goats. My independent variable was continuous data that I divided into 3 classes to make it categorical: group 1= <30%, group 2= 30% to 70%, and group 3= >70%. However, my worry is that prediction accuracy may be compromised by images from goats with output values close to the border, eg 31%. To address this issue, I would want to use multiple label classification but only for images from goats with percentages within +/- 5% from the border ie 25% to 35% and 65%-75%. I would not want to maintain 3 groups.
My question is that, is this possible. if so, how do I group my training and testing data set, which Multi-label classification method would best fit my problem and how do I go about it