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lrns = list(9 ]& ]- S( ^+ Y" V6 e1 \* t: z' W
makeLearner("classif.lda", id = "lda"),7 ?) [+ l% S" K; h
makeLearner("classif.rpart", id = "rpart"),+ ^# ]1 e$ B. V& q
makeLearner("classif.randomForest", id = "randomForest")3 p- B" b' C6 Z1 q
)3 q0 I* [$ Q: t; `/ b N
) Z) ^& K" `9 e
## Get additional Tasks from package mlbench. h* h! `$ { Q7 S( I$ e
ring.task = convertMLBenchObjToTask("mlbench.ringnorm", n = 10)
+ F0 f7 o% v* t. l% iwave.task = convertMLBenchObjToTask("mlbench.waveform", n = 10)& i* W+ ?6 [" N7 g7 z7 V
0 w$ d& H% K9 T* f$ D1 `
tasks = list(iris.task, sonar.task, pid.task, ring.task, wave.task)
6 n! l$ |- N; erdesc = makeResampleDesc("CV", iters = 3)
. f# d% G7 ~# o; I7 r7 ameas = list(mmce, ber, timetrain)1 M- H8 i# `9 T5 z7 B$ k$ l
bmr = benchmark(lrns, tasks, rdesc, meas, show.info = FALSE)
" B T1 V( u E/ C: u5 Sbmr6 c/ {! f6 C9 W9 P
plotBMRBoxplots(bmr, measure = ber, style = "violin", pretty.names = FALSE) +
4 J# |4 z o# F5 Y' u* Z$ j aes(color = learner.id) +
, ~5 j# f; t: T theme(strip.text.x = element_text(size = 8)) |
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