For this engine, there are multiple modes: classification and regression
This model has no tuning parameters.
Engine arguments of interest
max_runtime_secs
and max_models
: controls the maximum running time and number of models to build in the automatic process.
exclude_algos
and include_algos
: a character vector indicating the excluded or included algorithms during model building. To see a full list of supported models, see the details section in [h2o::h2o.automl()].
validation
: An integer between 0 and 1 specifying the proportion of training data reserved as validation set. This is used by h2o for performance assessment and potential early stopping.
[agua::h2o_train_auto()] is a wrapper around [h2o::h2o.automl()].
auto_ml() %>%
set_engine("h2o") %>%
set_mode("regression") %>%
translate()
## Automatic Machine Learning Model Specification (regression)
##
## Computational engine: h2o
##
## Model fit template:
## agua::h2o_train_auto(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
## validation_frame = missing_arg(), verbosity = NULL)
auto_ml() %>%
set_engine("h2o") %>%
set_mode("classification") %>%
translate()
## Automatic Machine Learning Model Specification (classification)
##
## Computational engine: h2o
##
## Model fit template:
## agua::h2o_train_auto(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
## validation_frame = missing_arg(), verbosity = NULL)
Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.
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()
.
Models fitted with this engine may require native serialization methods to be properly saved and/or passed between R sessions. To learn more about preparing fitted models for serialization, see the bundle package.
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