I am using model slices as shown here. However the example hardcodes the base_score to 0.5 for training hence it knows the value during prediction. I have a library metric that does not know the base_score during training. The metric gets the trained booster. From using both the scikit interface and the xgb native interface I cannot find how to get the base_score that was calculated or used during training. The model.get_xgb_params() gives back base_score: None, which in this case was the value use in the model training.
Is there a method to get the base_score that was calculated or used during training in a saved model?