# How could I do parameter tuning with feature selection in R package mlr?

In this project, I am trying to tune the parameters(especially the step number parameter) of the CoxBoost model for survival analysis. I have more features than samples and many features are highly correlated. So I made a filtering function which allows me to do an initial feature selection. The next step, I want to find the optimal parameter for my model. How could I achieve that?

Codes without initial selection

ps = makeParamSet(
makeDiscreteParam("stepno", values = c(50,70,100,150,200))
)
ctrl = makeTuneControlGrid()
rdesc=makeResampleDesc("RepCV",folds=3L,reps=20L, predict = "both")
rdesc, par.set = ps, control = ctrl)


Codes with initial selection

ps = makeParamSet(
makeDiscreteParam("stepno", values = c(50,70,100,150,200))
)
ctrl = makeTuneControlGrid()
rdesc=makeResampleDesc("RepCV",folds=3L,reps=20L, predict = "both")
lrn = makeTuneWrapper("surv.CoxBoost", resampling = rdesc,
par.set = ps, control = ctrl, show.info = FALSE)
lrn1 = makeFilterWrapper(learner = lrn, fw.method =
"uni.cor.filter",fw.threshold=0.5)
rdesc, par.set = ps, control = ctrl)


lrn = makeFilterWrapper("surv.CoxBoost", fw.method = "uni.cor.filter",fw.threshold=0.5)
lrn1 = makeTuneWrapper(lrn,
resampling = makeResampleDesc("RepCV",folds=3L,reps=20L),
par.set = makeParamSet(
makeDiscreteParam("stepno", values = c(50,70,100,150,200))),
control = makeTuneControlGrid(),
show.info = T)