Probabilities predicted by XGBoost 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
If the predicted outcome targets(i.e., probabilities in this example), are consistently higher than actual, it implies you have statistical bias model. Statistical bias is when there is a systematically different result from the population parameter being estimated.
There are many ways to reduce statistical bias:

Resample at more extreme ratio
Change the loss function to more heavily penalize mispredictions
Change algorithm