h2o::h2o.automl defines an automated model training process and returns a leaderboard of models with best performances.
For this engine, there are multiple modes: classification and regression
This model has no tuning parameters.
Engine arguments of interest
max_models: controls the maximum running time
and number of models to build in the automatic process.
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
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
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
fit(), parsnip will
convert factor columns to indicators.
To use the h2o engine with tidymodels, please run
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
You can control the number of threads in the thread pool used by h2o
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
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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