When ML algorithms, e.g. Vowpal Wabbit or some of the factorization machines winning click through rate competitions (Kaggle), mention that features are 'hashed', what does that actually mean for the model? Lets say there is a variable that represents the ID of an internet add, which takes on values such as '236BG231'. Then I understand that this feature is hashed to a random integer. But, my question is:
- Is the integer now used in the model, as an integer (numeric) OR
- is the hashed value actually still treated like a categorical variable and one-hot-encoded? Thus the hashing trick is just to save space somehow with large data?