h2o::h2o.automl defines an automated model training process and returns a leaderboard of models with best performances.
Details
For this engine, there are multiple modes: classification and regression
Tuning Parameters
This model has no tuning parameters.
Engine arguments of interest
max_runtime_secs
andmax_models
: controls the maximum running time and number of models to build in the automatic process.exclude_algos
andinclude_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 inh2o::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.
Translation from parsnip to the original package (regression)
agua::h2o_train_auto()
is a wrapper around
h2o::h2o.automl()
.
Preprocessing requirements
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 fit()
, parsnip will
convert factor columns to indicators.
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()
.