I have thousands of records where each is a unique class. I think what this means is that, which ever ML algorithm I choose, I will not be able to execute a train/test split because the test split cannot be modelled. This is because there is only one record for each class, therefore there is no way the model could have trained for anything that is in the test split.
I can't justify introducing unseen records for prediction if my model cannot be validated.
Each record is similar to a generic accounting record with about 20 features. I thought about using KNN, but KNN would not be able to do a train/test split neither. How do I go about mitigating this? Is KNN the wrong model to use here?
question from:
https://stackoverflow.com/questions/65878928/classification-problem-every-record-is-a-unique-class 与恶龙缠斗过久,自身亦成为恶龙;凝视深渊过久,深渊将回以凝视…