This function is a very parallel version of
benchmark using batchtools.
Experiments are created in the provided registry for each combination of
learners, tasks and resamplings. The experiments are then stored in a registry and the
runs can be started via
submitJobs. A job is one train/test split
of the outer resampling. In case of nested resampling (e.g. with
each job is a full run of inner resampling, which can be parallelized in a second step
with ParallelMap. For details on the usage and support backends have
a look at the batchtools tutorial page:
The general workflow with
batchmark looks like this:
Create an ExperimentRegistry using
batchmark(...) which defines jobs for all learners and tasks in an
Submit jobs using
Babysit the computation, wait for all jobs to finish using
reduceBatchmarkResult() to reduce results into a
If you want to use this with OpenML datasets you can generate tasks from a vector
of dataset IDs easily with
tasks = lapply(data.ids, function(x) convertOMLDataSetToMlr(getOMLDataSet(x))).
data.table]. Generated job ids are stored in the column “job.id”.
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