Say I have a dataset of labeled elements and an unlabeled dataset that I would like to apply my machine learning model to after training. How would I go about reporting accuracy/ROC AUC/etc. in this scenario. For example:
Labeled Train Dataset: [0, 1, 2] => 3 [1, 3, 5] => 9 Labeled Test Dataset: [2, 5, 1] => 8 Unlabeled Datset: [6, 7, 8]
Even though there is no known ground truth for the element in the Unlabeled Dataset, I would still like to report it.
Would I train the model on Labeled Train Dataset and report accuracy/loss/etc. based on the Labeled Test Dataset, then retrain the model using using both the Labeled Train Dataset and Labeled Test Dataset, and report the output of the Unlabeled Dataset based on this new retrained model? This would mean that the two things reported are not from the "same" model, but I am not sure of any other way to utilize all of the data for predicting on the Unlabeled Dataset while still providing evaluation metrics. Does any have suggestions?