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lrns = list($ M/ \% \6 r. r; i: J4 e$ J2 ~9 G
makeLearner("classif.lda", id = "lda")," C+ |7 o# d& b# p% T+ Y4 U) X. d
makeLearner("classif.rpart", id = "rpart"),
; d/ ]3 L! {+ Y8 l$ Q7 M makeLearner("classif.randomForest", id = "randomForest")
7 p' E' R4 R: s9 X4 ]8 r- K)9 t1 T4 v" D" B# p$ G& ~
% A% Y. d( ]* V/ ~" h( t## Get additional Tasks from package mlbench
- I5 P0 x% u' v0 Rring.task = convertMLBenchObjToTask("mlbench.ringnorm", n = 10)) Q- T. R8 ^1 X* h( Y
wave.task = convertMLBenchObjToTask("mlbench.waveform", n = 10)% m! h: R! C. b* t- ]6 ?0 v, `
8 l6 K8 R! ]8 @$ `: F* i2 ~
tasks = list(iris.task, sonar.task, pid.task, ring.task, wave.task): f6 T7 p$ T1 J% m- N9 D- z) c
rdesc = makeResampleDesc("CV", iters = 3), q" T( g1 o. k4 T
meas = list(mmce, ber, timetrain)' {. v# b0 S: K9 r7 t1 x
bmr = benchmark(lrns, tasks, rdesc, meas, show.info = FALSE)1 T" O: M( \9 ], V
bmr
' H* M* I' V. NplotBMRBoxplots(bmr, measure = ber, style = "violin", pretty.names = FALSE) +
3 e7 Z- h9 `# I- \9 j aes(color = learner.id) +$ l. h' {9 N& u& K$ F @
theme(strip.text.x = element_text(size = 8)) |
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