Scikit learn pipelines. Does it make any sense?
I have used pipelines recently for data exploration purposes.
I wanted to random search different pipelines.
This could be at least one reason to use pipelines.
But you are right pipelines aren't verry useful for many other purposes.
A noob question.
As I understand, the pipeline library of scikit learn is a kind of automation helper, which brings the data through a defined data processing cycle. But in this case I don't see any sense in it.
Why can't I implement data preparation, model training, score estimation, etc. via functional or OOP programming in python? For me it seems much more agile and simple, you can control all inputs, adjust complex dynamic parameter grids, evaluate complex metrics, etc.
Can you tell me, why should anyone use sklearn.pipelines? Why does it exist?