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lrns = list(: b8 U' V0 `* t) x7 z
makeLearner("classif.lda", id = "lda"),- X; t% K/ e0 \. J
makeLearner("classif.rpart", id = "rpart"),8 L; S) E) Y' `- Y! A I; }+ m8 g. ^4 u
makeLearner("classif.randomForest", id = "randomForest")
8 M+ a* J/ ^: k) b0 N' D)
* i- w" Y% u$ }7 G8 J
' K0 A, M f8 ?## Get additional Tasks from package mlbench
/ s! P x! m9 O4 Y4 ~ring.task = convertMLBenchObjToTask("mlbench.ringnorm", n = 10)7 j u0 o) ]* ^& J0 N) Q
wave.task = convertMLBenchObjToTask("mlbench.waveform", n = 10)6 n6 {% K7 X3 d7 d. d8 h
" u" l6 L( g) A: C, q2 |
tasks = list(iris.task, sonar.task, pid.task, ring.task, wave.task)) s' _' w. e" ?: @
rdesc = makeResampleDesc("CV", iters = 3)
' ]: U/ b6 a2 [% Z! i+ |& m( q5 omeas = list(mmce, ber, timetrain)# U2 L l. F, A
bmr = benchmark(lrns, tasks, rdesc, meas, show.info = FALSE)
' n8 S i, q) Q+ `5 h3 Ibmr+ w7 H8 [6 x9 u. `, H) r
plotBMRBoxplots(bmr, measure = ber, style = "violin", pretty.names = FALSE) +
9 N7 a7 C- t% ^: Q9 {* K aes(color = learner.id) + K& p/ r0 _/ R+ ?: G. {( p
theme(strip.text.x = element_text(size = 8)) |
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