CODEWITHSUNDEEP
rh2o
Raunak
Raunak

Reputation: 3

Extract sample of features used to build each tree in H2O

In GBM model, following parameters are used -

I understand how the sampling works and how many variables get considered for splitting at each level for every tree. I am trying to understand how many times each feature gets considered for making a decision. Is there a way to easily extract all sample of features used for making a splitting decision from the model object?

Referring to the explanation provided by H2O, http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/algo-params/col_sample_rate.html, is there a way to know 60 randomly chosen features for each split?

Thank you for your help!

Upvotes: 0

Views: 192

Answers (2)

Lauren
Lauren

Reputation: 5778

If you want to see which features were used at a given split in a give tree you can navigate the H2OTree object.

For R see documentation here and here

For Python see documentation here

You can also take a look at this Blog (if this link ever dies just do a google search for H2OTree class)

Upvotes: 1

TomKraljevic
TomKraljevic

Reputation: 3671

I don’t know if I would call this easy, but the MOJO tree visualizer spits out a graphviz dot data file which is turned into a visualization. This has the information you are interested in.

http://docs.h2o.ai/h2o/latest-stable/h2o-genmodel/javadoc/overview-summary.html#viewing-a-mojo

Upvotes: 0

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