Add H2O GBM support for boost_tree()#250
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This adds tidypredict support for H2O gradient boosting models (
H2ORegressionModel,H2OBinomialModel,H2OMultinomialModel), includingboost_tree()parsnip models fitted with agua's"h2o_gbm"engine, for both regression and classification. Trees are pulled from the running H2O cluster and turned into nestedcase_when()expressions, so a liveh2o.init()connection is required to build or evaluate a formula. Scope is limited to H2O's GBM (not its XGBoost) and the gaussian, bernoulli, and multinomial distributions. Addresses part of #232.Test plan
H2O tests require a running cluster and are skipped otherwise. To run them locally with a cluster available:
Rscript -e "devtools::test(filter = '^h2o')"andRscript -e "devtools::test(filter = '^tidymodels')".