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defining pipelines #8
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I feel even that would be a very redundant thing to do. Say, there are 3 transformations and 5 estimators, imagine having 15 combinations defined in the class that you are referring to. And, this is just the tip of the iceberg. Rather, defining the default parameters, and overriding them with user arguments is a more decent way to go ahead. |
No we just need to define them in pipeline, not build the pipeline with them. In that case we won't need to make permutations and combinations at all. We just need to list them for better reusuability. |
But that can't be done in abstract class. |
I am still trying to wrap my head around this. The current design, I think helps leave all the choices at the meta-level. What do you say, @Anirudh-Kakarlapudi ? |
Yeah cool, let's continue with the current plan. I was under the idea that we can't have code inside abstract methods, as in Java. But I need to check again if that's true in python, as we have been doing that already. If that is the case it does not make any difference. |
So, how it works here is: |
I think it will be quite tedious to include many transformations and estimators for each model. So, we should find a way to define them at one class and we can use subsets of these transformations and estimators for each specific implementation like linear regression.
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