Benchmark experiment for multiple learners and tasks.

Complete benchmark experiment to compare different learning algorithms across one or more tasks w.r.t. a given resampling strategy. Experiments are paired, meaning always the same training / test sets are used for the different learners. Furthermore, you can of course pass “enhanced” learners via wrappers, e.g., a learner can be automatically tuned using makeTuneWrapper.

benchmark(
  learners,
  tasks,
  resamplings,
  measures,
  keep.pred = TRUE,
  keep.extract = FALSE,
  models = FALSE,
  show.info = getMlrOption("show.info")
)

Arguments

learners	(list of Learner | character) Learning algorithms which should be compared, can also be a single learner. If you pass strings the learners will be created via makeLearner.
tasks	list of Task Tasks that learners should be run on.
resamplings	(list of ResampleDesc | ResampleInstance) Resampling strategy for each tasks. If only one is provided, it will be replicated to match the number of tasks. If missing, a 10-fold cross validation is used.
measures	(list of Measure) Performance measures for all tasks. If missing, the default measure of the first task is used.
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.
models	(logical(1)) Should all fitted models be stored in the ResampleResult? Default is FALSE.
show.info	(logical(1)) Print verbose output on console? Default is set via configureMlr.

Value

BenchmarkResult.

See also

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

Examples

lrns = list(makeLearner("classif.lda"), makeLearner("classif.rpart"))
tasks = list(iris.task, sonar.task)
rdesc = makeResampleDesc("CV", iters = 2L)
meas = list(acc, ber)
bmr = benchmark(lrns, tasks, rdesc, measures = meas)
#> Task: iris-example, Learner: classif.lda
#> Resampling: cross-validation
#> Measures:             acc       ber       
#> [Resample] iter 1:    1.0000000 0.0000000 
#> [Resample] iter 2:    0.9600000 0.0411111 
#> 
#> Aggregated Result: acc.test.mean=0.9800000,ber.test.mean=0.0205556
#> 
#> Task: Sonar-example, Learner: classif.lda
#> Resampling: cross-validation
#> Measures:             acc       ber       
#> [Resample] iter 1:    0.7019231 0.2980769 
#> [Resample] iter 2:    0.7788462 0.2133710 
#> 
#> Aggregated Result: acc.test.mean=0.7403846,ber.test.mean=0.2557240
#> 
#> Task: iris-example, Learner: classif.rpart
#> Resampling: cross-validation
#> Measures:             acc       ber       
#> [Resample] iter 1:    0.9600000 0.0394872 
#> [Resample] iter 2:    0.9333333 0.0672222 
#> 
#> Aggregated Result: acc.test.mean=0.9466667,ber.test.mean=0.0533547
#> 
#> Task: Sonar-example, Learner: classif.rpart
#> Resampling: cross-validation
#> Measures:             acc       ber       
#> [Resample] iter 1:    0.7019231 0.2980769 
#> [Resample] iter 2:    0.6923077 0.3054614 
#> 
#> Aggregated Result: acc.test.mean=0.6971154,ber.test.mean=0.3017692
#> 
rmat = convertBMRToRankMatrix(bmr)
print(rmat)
#>               Sonar-example iris-example
#> classif.lda               1            1
#> classif.rpart             2            2
plotBMRSummary(bmr)
plotBMRBoxplots(bmr, ber, style = "violin")
#> Warning: `fun.y` is deprecated. Use `fun` instead.
#> Warning: `fun.ymin` is deprecated. Use `fun.min` instead.
#> Warning: `fun.ymax` is deprecated. Use `fun.max` instead.
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: Computation failed in `stat_ydensity()`:
#> replacement has 1 row, data has 0
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: Computation failed in `stat_ydensity()`:
#> replacement has 1 row, data has 0
plotBMRRanksAsBarChart(bmr, pos = "stack")
friedmanTestBMR(bmr)
#> 
#> 	Friedman rank sum test
#> 
#> data:  acc.test.mean and learner.id and task.id
#> Friedman chi-squared = 2, df = 1, p-value = 0.1573
#> 
friedmanPostHocTestBMR(bmr, p.value = 0.05)
#> Loading required package: PMCMRplus
#> Warning: Cannot reject null hypothesis of overall Friedman test,
#>              returning overall Friedman test.
#> 
#> 	Friedman rank sum test
#> 
#> data:  acc.test.mean and learner.id and task.id
#> Friedman chi-squared = 2, df = 1, p-value = 0.1573
#>