My first understanding was that batch_size is basically all needed to first test the model(s) on new incoming data and then train it/them on the new data. So, how does n_wait influence that procedure?
Docs: evaluate_prequential
My first guess would be that n_wait does not change the procedure, but only influences how the metrics are caculated. Would you agree?
Bonus: is there a integrated way to handle variable batch sizes in multiflow?
The batch_size parameter corresponds to the number of data samples passed to the model(s) on each test and train operation.
As you mention, the n_wait parameter is used to control the amount of data to be considered when evaluating the "current" performance (last n samples). Additionally, it is used to control the refresh rate of the evaluation plot.
For the bonus question, EvaluatePrequential does not support variable batch sizes. However, available learning methods can handle this scenario.
