details_auto_ml_h2o.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/auto_ml_h2o.R
\name{details_auto_ml_h2o}
\alias{details_auto_ml_h2o}
\title{Automatic machine learning via h2o}
\description{
\link[h2o:h2o.automl]{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
\subsection{Tuning Parameters}{

This model has no tuning parameters.

Engine arguments of interest
\itemize{
\item \code{max_runtime_secs} and \code{max_models}: controls the maximum running time
and number of models to build in the automatic process.
\item \code{exclude_algos} and \code{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
\code{\link[h2o:h2o.automl]{h2o::h2o.automl()}}.
\item \code{validation}: An integer between 0 and 1 specifying the \emph{proportion}
of training data reserved as validation set. This is used by h2o for
performance assessment and potential early stopping.
}
}

\subsection{Translation from parsnip to the original package (regression)}{

\code{\link[agua:h2o_train]{agua::h2o_train_auto()}} is a wrapper around
\code{\link[h2o:h2o.automl]{h2o::h2o.automl()}}.

\if{html}{\out{<div class="sourceCode r">}}\preformatted{auto_ml() \%>\%  
  set_engine("h2o") \%>\% 
  set_mode("regression") \%>\% 
  translate()
}\if{html}{\out{</div>}}

\if{html}{\out{<div class="sourceCode">}}\preformatted{## 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)
}\if{html}{\out{</div>}}
}

\subsection{Translation from parsnip to the original package (classification)}{

\if{html}{\out{<div class="sourceCode r">}}\preformatted{auto_ml() \%>\%  
  set_engine("h2o") \%>\% 
  set_mode("classification") \%>\% 
  translate()
}\if{html}{\out{</div>}}

\if{html}{\out{<div class="sourceCode">}}\preformatted{## 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)
}\if{html}{\out{</div>}}
}

\subsection{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 \code{\link[=fit.model_spec]{fit()}}, parsnip will
convert factor columns to indicators.
}

\subsection{Initializing h2o}{

To use the h2o engine with tidymodels, please run \code{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
\code{\link[h2o:h2o.init]{h2o::h2o.init()}}.

You can control the number of threads in the thread pool used by h2o
with the \code{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
\code{h2o::h2o.shutdown()}.
}

\subsection{Saving fitted model objects}{

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.
}
}
\keyword{internal}