r descr_models("boost_tree", "h2o")
defaults <- tibble::tibble(parsnip = c("mtry", "trees", "tree_depth", "learn_rate", "sample_size", "min_n", "loss_reduction", "stop_iter"), default = c(1, 50L, 6, 0.3, 1, 1, 0, 0)) # For this model, this is the same for all modes param <- boost_tree() %>% set_engine("h2o") %>% set_mode("regression") %>% make_parameter_list(defaults)
This model has r nrow(param)
tuning parameters:
param$item
min_n
represents the fewest allowed observations in a terminal node, [h2o::h2o.xgboost()] allows only one row in a leaf by default.
stop_iter
controls early stopping rounds based on the convergence of the engine parameter stopping_metric
. By default, [h2o::h2o.xgboost()] does not use early stopping. When stop_iter
is not 0, [h2o::h2o.xgboost()] uses logloss for classification, deviance for regression and anonomaly score for Isolation Forest. This is mostly useful when used alongside the engine parameter validation
, which is the proportion of train-validation split, parsnip will split and pass the two data frames to h2o. Then [h2o::h2o.xgboost()] will evaluate the metric and early stopping criteria on the validation set.
[agua::h2o_train_xgboost()] is a wrapper around [h2o::h2o.xgboost()].
r uses_extension("boost_tree", "h2o", "regression")
boost_tree( mtry = integer(), trees = integer(), tree_depth = integer(), learn_rate = numeric(), min_n = integer(), loss_reduction = numeric(), stop_iter = integer() ) %>% set_engine("h2o") %>% set_mode("regression") %>% translate()
r uses_extension("boost_tree", "h2o", "classification")
boost_tree( mtry = integer(), trees = integer(), tree_depth = integer(), learn_rate = numeric(), min_n = integer(), loss_reduction = numeric(), stop_iter = integer() ) %>% set_engine("h2o") %>% set_mode("classification") %>% translate()
Non-numeric predictors (i.e., factors) are internally converted to numeric. In the classification context, non-numeric outcomes (i.e., factors) are also internally converted to numeric.
mtry
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