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I have some ideas about how to help restructure the parameters that we list on the algorithm user guide pages (we can discuss more and then refine this ticket...). There's a lot of duplication between the algorithm pages, and currently we can't tell what parameters are common algo params vs hyperparams that are unique to the specific algorithm (e.g. sample_rate, mu_factor, etc).
We could break the param lists on the page into two groups: common algo params & algo-specific params.
We could also consider moving all the common algo params to their own user guide page and then we can keep one copy of them. I think what's the most interesting when a user visits an algo page is how to tune that particular algorithm, not the generic data/algo params like: x, y, training_frame, nfolds, seed, etc. This would also make the user guide pages shorter.
I have some ideas about how to help restructure the parameters that we list on the algorithm user guide pages (we can discuss more and then refine this ticket...). There's a lot of duplication between the algorithm pages, and currently we can't tell what parameters are common algo params vs hyperparams that are unique to the specific algorithm (e.g. sample_rate, mu_factor, etc).
We could break the param lists on the page into two groups: common algo params & algo-specific params.
We could also consider moving all the common algo params to their own user guide page and then we can keep one copy of them. I think what's the most interesting when a user visits an algo page is how to tune that particular algorithm, not the generic data/algo params like: x, y, training_frame, nfolds, seed, etc. This would also make the user guide pages shorter.
Lastly, the order of the common algo params are a bit weird (starting with optional model_id). We should do a more sensible ordering (e.g. http://docs.h2o.ai/h2o/latest-stable/h2o-r/docs/reference/h2o.xgboost.html)
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