I am tuning more than 2 hyperparameters, while Generate hyperparameter effect data using the function generateHyperParsEffectData I set partial.dep = TRUE, while plotting plotHyperParsEffect i am getting error for classification learner, its requiring regressor learner
This is my task and learner for classification
classif.task <- makeClassifTask(id = "rfh2o.task", data = Train_clean, target = "Action")
rfh20.lrn.base = makeLearner("classif.h2o.randomForest", predict.type = "prob",fix.factors.prediction=TRUE)
rfh20.lrn <- makeFilterWrapper(rfh20.lrn.base, fw.method = "chi.squared", fw.perc = 0.5)
This is my tuning
rdesc <- makeResampleDesc("CV", iters = 3L, stratify = TRUE)
ps<- makeParamSet(makeDiscreteParam("fw.perc", values = seq(0.2, 0.8, 0.1)),
makeIntegerParam("mtries", lower = 2, upper = 10),
makeIntegerParam("ntrees", lower = 20, upper = 50)
)
Tuned_rf <- tuneParams(rfh20.lrn, task = QBE_classif.task, resampling = rdesc.h2orf, par.set = ps.h2orf, control = makeTuneControlGrid())
While plotting the tune
h2orf_data = generateHyperParsEffectData(Tuned_rf, partial.dep = TRUE)
plotHyperParsEffect(h2orf_data, x = "iteration", y = "mmce.test.mean", plot.type = "line", partial.dep.learn =rfh20.lrn)
I am getting the Error
Error in checkLearner(partial.dep.learn, "regr") :
Learner 'classif.h2o.randomForest.filtered' must be of type 'regr', not: 'classif'
I would expect to see the plot for any more tuning requirement so I can add more hyper tuning, am I missing some thing.