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lrns = list(
) m" g8 c8 A% y makeLearner("classif.lda", id = "lda"),3 w- W( d. X; z- z+ n# Q
makeLearner("classif.rpart", id = "rpart"),
; f! x8 B9 [: Y+ c. V makeLearner("classif.randomForest", id = "randomForest")
. l; T! W# a& E% x)
' e/ ~/ p+ n: w! L ?" M; `/ P* P/ e
## Get additional Tasks from package mlbench
& d/ J: O! ^9 P( b& A: hring.task = convertMLBenchObjToTask("mlbench.ringnorm", n = 10)" L: A1 e- j/ R
wave.task = convertMLBenchObjToTask("mlbench.waveform", n = 10)
4 n% R J. F) ]6 i8 A8 ]
: d# D* A( e R6 k# R' _% W5 wtasks = list(iris.task, sonar.task, pid.task, ring.task, wave.task)
/ k! S3 Y5 A5 R- ^rdesc = makeResampleDesc("CV", iters = 3)# |% U: E t: B* c$ `
meas = list(mmce, ber, timetrain)
& [) U/ U$ |7 k* Nbmr = benchmark(lrns, tasks, rdesc, meas, show.info = FALSE)
0 Y/ a1 h0 A& M. h8 C1 pbmr$ b# F/ T( z4 I/ O
plotBMRBoxplots(bmr, measure = ber, style = "violin", pretty.names = FALSE) +
' g- n. X; H$ x! ]" Q9 Z# W! ] aes(color = learner.id) +6 Q1 D( `; g0 f9 d3 O- l
theme(strip.text.x = element_text(size = 8)) |
|