I'm trying to understand how cross-validation works. I've looked at different processes showing cross-validation. This one process is by far the most interesting. I'm imagining what it must be like inside these operators trying to get the parameters correct. I assume this process is the gold standard when it comes to understanding how cross-validation works.
I compared this with the AutoModel results classification errors over eight different models.
As a data analyst working for a boss who wants results yesterday, what is there to gain risking errors building such a process, when I could run AutoModel?
I just noticed one of the operators in the process creates a lift chart. In AutoModel each model comes with its own lift chart.
Please advise. Thanks for your time.
