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lrns = list(+ N* b0 @8 u5 u. \4 R2 c" d; j
makeLearner("classif.lda", id = "lda"),( Z% l9 l+ k4 V6 W& e
makeLearner("classif.rpart", id = "rpart"),2 b. m7 m- G( S' `
makeLearner("classif.randomForest", id = "randomForest")
( l% a2 o, c3 R7 ?3 ^)4 m! r3 ?9 W/ Q" _7 u% t" \
4 k( n% B3 _# }$ W& O) ^3 ]7 q
## Get additional Tasks from package mlbench
! P4 x/ H$ W% ?) `9 X: n* Kring.task = convertMLBenchObjToTask("mlbench.ringnorm", n = 10)7 Y2 R: H; G% w" t
wave.task = convertMLBenchObjToTask("mlbench.waveform", n = 10)
: U+ E( a2 U8 H3 |: ~$ q% X. r, B2 g; P
tasks = list(iris.task, sonar.task, pid.task, ring.task, wave.task)
4 e- L. y) U0 b! C2 k* \. j }rdesc = makeResampleDesc("CV", iters = 3)) I, Y8 K% J _- J% F
meas = list(mmce, ber, timetrain)1 ^+ F- i- W& ]
bmr = benchmark(lrns, tasks, rdesc, meas, show.info = FALSE)
! h3 F, E1 { B$ ]bmr
/ v" M/ y2 g+ O' ]plotBMRBoxplots(bmr, measure = ber, style = "violin", pretty.names = FALSE) + c2 U1 o S% c
aes(color = learner.id) +
8 J7 y- |9 k G' V+ {' T, I1 A theme(strip.text.x = element_text(size = 8)) |
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