##### Parameter set of operators for hyperparameter tuning:
ps.ksvm = makeParamSet(
makeNumericParam("C", lower = -15, upper = 15, trafo = function(x) 2^x),
makeNumericParam("sigma", lower = -15, upper = 15, trafo = function(x) 2^x))
ps.ranger = makeParamSet(
makeNumericParam("mtry", lower = 1/10, upper = 1/1.5), ## range(p/10, p/1.5), p is the number of features
makeNumericParam("sample.fraction", lower = .1, upper = 1))
ps.xgboost = makeParamSet(
makeNumericParam("eta", lower = .001, upper = .3),
makeIntegerParam("max_depth", lower = 1L, upper = 15L),
makeNumericParam("subsample", lower = 0.5, upper = 1),
makeNumericParam("colsample_bytree", lower = 0.5, upper = 1),
makeNumericParam("min_child_weight", lower = 0, upper = 50)
)
ps.kknn = makeParamSet(makeIntegerParam("k", lower = 1L, upper = 20L))
ps.naiveBayes = makeParamSet(makeNumericParam("laplace", lower = 0.01, upper = 100))
ps.filter = makeParamSet(makeNumericParam("perc", lower = .1, upper = 1))
ps.pca = makeParamSet(makeNumericParam("rank", lower = .1, upper = 1)) ## range(p/10, p/1.5), p is the number of features
##### Get parameter set for generated model:
g_getParamSetFun = function(model) {
ps.classif = sub(pattern = "classif", model[3], replacement = "ps")
ps.classif = eval(parse(text = ps.classif)) # hyperparameter set for classifier
if (model[2] == "NA") {
return(ps.classif)
} else if (length(grep(pattern = "perc", x = model)) > 0) {
return(c(ps.classif, ps.filter))
} else {
return(c(ps.classif, ps.pca))
}
}
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