Reduce results of a batch-distributed benchmark.

This creates a BenchmarkResult from a batchtools::ExperimentRegistry. To setup the benchmark have a look at batchmark.

reduceBatchmarkResults(
  ids = NULL,
  keep.pred = TRUE,
  keep.extract = FALSE,
  show.info = getMlrOption("show.info"),
  reg = batchtools::getDefaultRegistry()
)

Arguments

ids	(data.frame or integer) A base::data.frame (or data.table::data.table) with a column named “job.id”. Alternatively, you may also pass a vector of integerish job ids. If not set, defaults to all successfully terminated jobs (return value of batchtools::findDone.
keep.pred	(logical(1)) Keep the prediction data in the pred slot of the result object. If you do many experiments (on larger data sets) these objects might unnecessarily increase object size / mem usage, if you do not really need them. The default is set to TRUE.
keep.extract	(logical(1)) Keep the extract slot of the result object. When creating a lot of benchmark results with extensive tuning, the resulting R objects can become very large in size. That is why the tuning results stored in the extract slot are removed by default (keep.extract = FALSE). Note that when keep.extract = FALSE you will not be able to conduct analysis in the tuning results.
show.info	(logical(1)) Print verbose output on console? Default is set via configureMlr.
reg	(batchtools::ExperimentRegistry) Registry, created by batchtools::makeExperimentRegistry. If not explicitly passed, uses the last created registry.

Value

(BenchmarkResult).

See also

Other benchmark: BenchmarkResult, batchmark(), benchmark(), convertBMRToRankMatrix(), friedmanPostHocTestBMR(), friedmanTestBMR(), generateCritDifferencesData(), getBMRAggrPerformances(), getBMRFeatSelResults(), getBMRFilteredFeatures(), getBMRLearnerIds(), getBMRLearnerShortNames(), getBMRLearners(), getBMRMeasureIds(), getBMRMeasures(), getBMRModels(), getBMRPerformances(), getBMRPredictions(), getBMRTaskDescs(), getBMRTaskIds(), getBMRTuneResults(), plotBMRBoxplots(), plotBMRRanksAsBarChart(), plotBMRSummary(), plotCritDifferences()