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What are most important parameters to tune for Deep Learning, XGBT, and Gen. Linear Model?

Fred12Fred12 Member Posts: 344 Unicorn
edited November 2018 in Help

hi,

 

I want to try out those 3 new algorithms that came with 7.2 on my dataset (4500 examples with 25 num. attributes), what are the most important parameters to tune in a grid optimization operator for them? and in what intervals? are there any experiences..?

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Answers

  • yyhuangyyhuang Administrator, Employee-RapidMiner, RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 364 RM Data Scientist

    No free hunch :smileywink:

     

    For deel learning, it basically depend on the network design and specific domain knowledge,

    how to choose activation function, # epochs, hidden layer sizes, learning rate, parameters for avoid overfitting etc....

     

    Why not download the booklet and take a look at the reference for the supervised models you just mentioned from 

    http://www.h2o.ai/docs/

    you will get more helpful information there

     

     

     

     

  • fritofrito Member Posts: 1 Learner III

    Yea I'd like to get some ideas about the best params and their ranges to start tweaking with. 

     

    I run some sweeps and was  rather disappointed.

     

    I cannot run 10 params so any pointers are welcome.

     

    Also surprisingly I get a better generalization of a smaller set than a bigger one (my total data set is just a few thousands of examples), what gives..?!

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