Let's say I have a multi-class set of data with 4 labels: [A, B, C, D] that I want to use an ovo SVC classifier to predict. I know that once I train, I'll have 6 ovo models trained and can use
svc.decision_function() to see the set of decision function outputs. Those functions are organized as
[AB, AC, AD, BC, BD, CD]
with the first letter being the positive case, and the second letter being the negative case. As I understand it, prediction takes these decision function scores and produces a set of "votes", the mode of which is the final prediction. Something like:
[AB, AC, AD, BC, BD, CD] #Model Organization
[1.4, 0.2, 0.6, 3, -2.2, -0.4] #Decision function output
[A, A, A, B, D, D] #Prediction for each model
The mode is A, so the svc.predict() function should output A. My question is what if the decision function output looks like this instead (I just flipped the sign of the 2nd entry but pretend its a new output):
[AB, AC, AD, BC, BD, CD] #Model Organization
[1.4, -0.2, 0.5, 3, -2.2, -0.4] #Decision function output
[A, C, A, B, D, D] #Prediction for each model
Now both A or D are the mode, so how would SVC break the tie and make the prediction? Speculatively I would sum the values of the decision function output (wrt the direction of the label) for each label, since the distance from the decision surface indicates to me how decisively the model predicted the label in each case. That would make D in the example. But I could also see using the specific score for the "AD" model. Regardless, not sure how SVC does it.
