question 1)
For a binary classification problem I could use num_labels
as 1 (positive or not) or 2 (positive and negative). Is there any guideline regarding which setting is better? It seems that if we use 1 then probability would be calculated using sigmoid
function and if we use 2 then probabilities would be calculated using softmax
function.
question 2)
In both cases are my y labels going to be same? each data point will have 0 or 1 and not one hot encoding? For example, if I have 2 data points then y would be 0,1
and not [0,0],[0,1]
I have very unbalanced classification problem where class 1 is present only 2% of times. In my training data I am oversampling