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I am currently considering Orange as the base for a meta-learning assistant prototype I intend to develop, but before committing myself to a thorough exploration of the documentation and learning about python development (which would both be quite time consuming), I would appreciate some insight regarding the feasibility of such prototype within Orange framework.

The main aim of the prototype I intend to develop is to allow efficient use of data mining and machine learning algorithm by non experts. Concretely, I wish as a first step to be able to give the user a workflow answering his modelling need, that I elicit from his dataset and expression of his need. In order to perform this elicitation, I intend to run a process that implies designing and executing learning workflows on his data.

Is it possible from within the Orange framework (or else from an above "supervising" framework) to automatically define and execute learning workflows ?

HaveNoDisplayName
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William R
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  • this question is better off on their mailing-list or forum, or maybe one related to the topic in general, then on this site. – hoijui Jul 09 '15 at 12:51
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    @hoijui Orange doesn't have an official forum or mailing list. :Y – K3---rnc Jul 10 '15 at 15:34

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Yes, it is.

We have actually experimented with a "recommendation system" that would suggest parts of the workflow to the user. It wasn't useful. Also, there have been various meta-learning projects in the past and I think that the general consensus is --- it doesn't work. ;)

But if you intend to try it, Orange is suitable platform for this.

@hoijui: Orange no longer has any other mailing list or forum, just this one. Developers follow Stack overflow and answer questions there.

JanezD
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  • Thank you for this answer ! Would you by chance have any link or reference toward the meta-learning projects or the recomendation system you're mentioning ? I would like to investigate those as some meta-learning experiments in the literature did have midly successfull results... – William R Jul 14 '15 at 14:57