h2o::h2o.xgboost() creates a series of decision trees
forming an ensemble. Each tree depends on the results of previous trees.
All trees in the ensemble are combined to produce a final prediction.
Details
For this engine, there are multiple modes: classification and regression
Tuning Parameters
This model has 8 tuning parameters:
trees: # Trees (type: integer, default: 50)tree_depth: Tree Depth (type: integer, default: 6)min_n: Minimal Node Size (type: integer, default: 1)learn_rate: Learning Rate (type: double, default: 0.3)sample_size: # Observations Sampled (type: integer, default: 1)mtry: # Randomly Selected Predictors (type: integer, default: 1)loss_reduction: Minimum Loss Reduction (type: double, default: 0)stop_iter: # Iterations Before Stopping (type: integer, default: 0)
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.
Translation from parsnip to the original package (regression)
agua::h2o_train_xgboost() is a wrapper
around h2o::h2o.xgboost().
The agua extension package is required to fit this model.
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()## Boosted Tree Model Specification (regression)
##
## Main Arguments:
## mtry = integer()
## trees = integer()
## min_n = integer()
## tree_depth = integer()
## learn_rate = numeric()
## loss_reduction = numeric()
## stop_iter = integer()
##
## Computational engine: h2o
##
## Model fit template:
## agua::h2o_train_xgboost(x = missing_arg(), y = missing_arg(),
## weights = missing_arg(), validation_frame = missing_arg(),
## col_sample_rate = integer(), ntrees = integer(), min_rows = integer(),
## max_depth = integer(), learn_rate = numeric(), min_split_improvement = numeric(),
## stopping_rounds = integer())Translation from parsnip to the original package (classification)
The agua extension package is required to fit this model.
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()## Boosted Tree Model Specification (classification)
##
## Main Arguments:
## mtry = integer()
## trees = integer()
## min_n = integer()
## tree_depth = integer()
## learn_rate = numeric()
## loss_reduction = numeric()
## stop_iter = integer()
##
## Computational engine: h2o
##
## Model fit template:
## agua::h2o_train_xgboost(x = missing_arg(), y = missing_arg(),
## weights = missing_arg(), validation_frame = missing_arg(),
## col_sample_rate = integer(), ntrees = integer(), min_rows = integer(),
## max_depth = integer(), learn_rate = numeric(), min_split_improvement = numeric(),
## stopping_rounds = integer())Preprocessing
This engine does not require any special encoding of the predictors.
Categorical predictors can be partitioned into groups of factor levels
(e.g. {a, c} vs {b, d}) when splitting at a node. Dummy variables
are not required for this model.
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.
Interpreting mtry
The mtry argument denotes the number of predictors that will be
randomly sampled at each split when creating tree models.
Some engines, such as "xgboost", "xrf", and "lightgbm", interpret
their analogue to the mtry argument as the proportion of predictors
that will be randomly sampled at each split rather than the count. In
some settings, such as when tuning over preprocessors that influence the
number of predictors, this parameterization is quite
helpful—interpreting mtry as a proportion means that [0, 1] is
always a valid range for that parameter, regardless of input data.
parsnip and its extensions accommodate this parameterization using the
counts argument: a logical indicating whether mtry should be
interpreted as the number of predictors that will be randomly sampled at
each split. TRUE indicates that mtry will be interpreted in its
sense as a count, FALSE indicates that the argument will be
interpreted in its sense as a proportion.
mtry is a main model argument for
boost_tree() and
rand_forest(), and thus should not have an
engine-specific interface. So, regardless of engine, counts defaults
to TRUE. For engines that support the proportion interpretation
(currently "xgboost" and "xrf", via the rules package, and
"lightgbm" via the bonsai package) the user can pass the
counts = FALSE argument to set_engine() to supply mtry values
within [0, 1].
Initializing h2o
To use the h2o engine with tidymodels, please run h2o::h2o.init()
first. By default, This connects R to the local h2o server. This needs
to be done in every new R session. You can also connect to a remote h2o
server with an IP address, for more details see
h2o::h2o.init().
You can control the number of threads in the thread pool used by h2o
with the nthreads argument. By default, it uses all CPUs on the host.
This is different from the usual parallel processing mechanism in
tidymodels for tuning, while tidymodels parallelizes over resamples, h2o
parallelizes over hyperparameter combinations for a given resample.
h2o will automatically shut down the local h2o instance started by R
when R is terminated. To manually stop the h2o server, run
h2o::h2o.shutdown().