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