I looking at football data and trying to predict whether a goal will occur using xgboost with objective binary: logistic.
My data is 1:10 unbalanced with no goals being more dominant. I have used smote or task.over in mlr package to oversample (with a factor of 4).
I train the model, tune and cross validate but the predictions seems reasonable (low aucpr of 30% but high in other stats).
However when I look at the probabilities predicted by the model, it much larger than the actual average. Is there anything that could cause this?
What are the probabilities? i.e class 1 probs are the probabilities that they are in class 1, so may not truly represent the probability to score a goal.
Thanks in advance