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lrns = list(
# O" o. w! N& _; f% V* }6 E" M makeLearner("classif.lda", id = "lda"),
! a7 f. N/ B u; T9 _2 N$ @8 D makeLearner("classif.rpart", id = "rpart"),' _& E( l: Q2 X( B) R" F
makeLearner("classif.randomForest", id = "randomForest")
/ F5 ~4 d$ R6 R)& _. A9 I6 C4 l2 @3 a
! ` l" Q1 ~; w. f* y, s" M; ^## Get additional Tasks from package mlbench3 S% y" q1 r+ ~# x- Y
ring.task = convertMLBenchObjToTask("mlbench.ringnorm", n = 10)( p# U- {2 T0 W3 F( l( W% X1 L
wave.task = convertMLBenchObjToTask("mlbench.waveform", n = 10)) c6 E8 l! ]% [ w% v% r1 D; N4 p7 k
2 s, s$ N s4 g* y' Y* {5 D
tasks = list(iris.task, sonar.task, pid.task, ring.task, wave.task)
T* Z8 q2 ~4 p- L/ i# Y$ Vrdesc = makeResampleDesc("CV", iters = 3)$ _6 ^2 |+ D5 U7 o8 _ Q w
meas = list(mmce, ber, timetrain)* u4 q% n" w' M) k) V2 E% W' H: Y! U
bmr = benchmark(lrns, tasks, rdesc, meas, show.info = FALSE)
% X. g- Q9 O& H' v$ l9 T4 X) b/ nbmr* R7 ] Y8 w0 C+ `# N6 [3 K
plotBMRBoxplots(bmr, measure = ber, style = "violin", pretty.names = FALSE) +
6 t( d/ }) N& [9 O' K9 c aes(color = learner.id) +
, U3 ~+ R7 z- k# M0 Q: c theme(strip.text.x = element_text(size = 8)) |
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