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



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..?


  • Options
    yyhuangyyhuang Administrator, Employee, 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 


    you will get more helpful information there





  • Options
    fritofrito Member Posts: 1 Contributor I

    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..?!

Sign In or Register to comment.