```
# \donttest{
set.seed(123)
# using the direct parameter of the SVM (which is already defined in the learner)
lrn = makeWeightedClassesWrapper("classif.ksvm", wcw.weight = 0.01)
res = holdout(lrn, sonar.task)
print(calculateConfusionMatrix(res$pred))
# using the observation weights of logreg
lrn = makeWeightedClassesWrapper("classif.logreg", wcw.weight = 0.01)
res = holdout(lrn, sonar.task)
print(calculateConfusionMatrix(res$pred))
# tuning the imbalancy param and the SVM param in one go
lrn = makeWeightedClassesWrapper("classif.ksvm", wcw.param = "class.weights")
ps = makeParamSet(
makeNumericParam("wcw.weight", lower = 1, upper = 10),
makeNumericParam("C", lower = -12, upper = 12, trafo = function(x) 2^x),
makeNumericParam("sigma", lower = -12, upper = 12, trafo = function(x) 2^x)
)
ctrl = makeTuneControlRandom(maxit = 3L)
rdesc = makeResampleDesc("CV", iters = 2L, stratify = TRUE)
res = tuneParams(lrn, sonar.task, rdesc, par.set = ps, control = ctrl)
print(res)
# print(res$opt.path)
# }
```

Run the code above in your browser using DataLab