I have a training data trainhaving distance and dest_zip_code as predictor variables to predict delivery_days. I am using ranger RF model to create the 'Quantile RF regression' model object. Please note that the dest_zip_code levels in the training_data are based on 6 months.
Now, I have two identical test sets test_A and test_B
test_Ahasdest_zip_codefrom last 2 months and levels are also based on last 2 months.test_Bhasdest_zip_codefrom last 2 months but levels are refactored to last 6 months (same levels as train data)
When I use the predict function on both the test sets with the same trained model object, at-least half of the predictions are different.
- Can someone help me understand how the different factoring levels of test data with same observations is affecting the predictions?
- Which one is theoretically correct?
