Re: [ML] [New Feature] Trainers as pipeline parameters that can be varied

[Alexey Zinoviev]

I think it's great proposal, I've created a ticket
https://issues.apache.org/jira/browse/IGNITE-9634



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Re: [ML] [New Feature] Trainers as pipeline parameters that can be varied

[dmitrievanthony]

In case HTML doesn't work I'll duplicate the message.

Hi all,

A machine learning pipeline implemented in
https://issues.apache.org/jira/browse/IGNITE-9158 (see discussion  here
http://apache-ignite-developers.2346864.n4.nabble.com/ML-Machine-Learning-Pipeline-Improvement-tt32772.html)
supports hyperparameters variation, but not trainers variation so far.

Our  reference-framework scikit-learn (according to  documentation
http://scikit-learn.org/stable/modules/pipeline.html#pipeline) allows to
variate trainers and preprocessors the following way:

>>> param_grid = dict(reduce_dim=[None, PCA(5), PCA(10)],
...   clf=[SVC(), LogisticRegression()],
...   clf__C=[0.1, 10, 100])
>>> grid_search = GridSearchCV(pipe, param_grid=param_grid)

I think it would be a great improvement for our ML pipeline.

Alexey Zinoviev, it would be awesome if you as an author of original ML
pipeline take a look at this proposal. 



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[ML] [New Feature] Trainers as pipeline parameters that can be varied

[dmitrievanthony]

Hi all,A machine learning pipeline implemented in  IGNITE-9158
   (see discussion  here

 
) supports hyperparameters variation, but not trainers variation so far.Our
reference-framework scikit-learn (according to  documentation
  ) allows to
variate trainers and preprocessors the following way:>>> param_grid =
dict(reduce_dim=[None, PCA(5),
PCA(10)],...   clf=[SVC(),
LogisticRegression()],...   clf__C=[0.1,
10, 100])>>> grid_search = GridSearchCV(pipe, param_grid=param_grid)I think
it would be a great improvement for our ML pipeline.Alexey Zinoviev, it
would be awesome if you as an author of original ML pipeline take a look at
this proposal.



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