I am building lost sales estimation model for out of stock days etc. using XGBoost. I am using simple logic of training model on data of normal days with ample inventory (when sales and demand are same) and then using trained model to predict demand on out of stock days. For model building I am splitting the normal days data into train and test datasets.
However I am getting a peculiar problem of very highly overestimated sales values of out of stock days. prediction for both train and test days are fine but only out of stock days prediction gives problem. Any tips what might be going wrong and how I can debug the problem in gradient tree type of model.