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lrns = list(* f- ]6 B7 {( c
makeLearner("classif.lda", id = "lda"),& m5 s* d; \( z& ^& f
makeLearner("classif.rpart", id = "rpart"),9 s* {7 d3 h2 o* \, v7 R f
makeLearner("classif.randomForest", id = "randomForest")
6 f/ w1 S& k5 P4 p4 \2 ?. Y)% A5 B- U0 O% }, q Z6 J
2 X1 K' p7 U1 l6 K5 U## Get additional Tasks from package mlbench
8 V. `( A) s7 ~" lring.task = convertMLBenchObjToTask("mlbench.ringnorm", n = 10)
4 t9 p' j" a+ i' iwave.task = convertMLBenchObjToTask("mlbench.waveform", n = 10)( R* N! z8 x. B2 h5 f* ~% k
' d, d# J1 K3 w; Y, C/ ctasks = list(iris.task, sonar.task, pid.task, ring.task, wave.task)2 p9 s* g/ _/ D* N3 ^6 d1 T% b4 n1 d
rdesc = makeResampleDesc("CV", iters = 3)" S: w6 c( L# _5 y, O" r6 b) |9 O& I- w
meas = list(mmce, ber, timetrain)
& T1 G+ c; `$ k% X; E; \bmr = benchmark(lrns, tasks, rdesc, meas, show.info = FALSE)
F; Z, H$ z1 r8 j, g4 rbmr
' N# n% ^0 J ~# J; {; XplotBMRBoxplots(bmr, measure = ber, style = "violin", pretty.names = FALSE) +
' ^" K! E4 X: ?! ~6 E1 Z aes(color = learner.id) ++ |( P) r/ l0 J% v1 z
theme(strip.text.x = element_text(size = 8)) |
|