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  3. Hyperparameter Tuning in R

Exercise

Evaluating hyperparameter tuning results

Here, you will evaluate the results of a hyperparameter tuning run for a decision tree trained with the rpart package. The knowledge_train_data dataset has already been loaded for you, as have the packages mlr and tidyverse. And the following code has also been run:

task <- makeClassifTask(data = knowledge_train_data, 
                        target = "UNS")

lrn <- makeLearner(cl = "classif.rpart", fix.factors.prediction = TRUE)

param_set <- makeParamSet(
  makeIntegerParam("minsplit", lower = 1, upper = 30),
  makeIntegerParam("minbucket", lower = 1, upper = 30),
  makeIntegerParam("maxdepth", lower = 3, upper = 10)
)

ctrl_random <- makeTuneControlRandom(maxit = 10)

Instructions 1/3

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  • Create a holdout validation resampling scheme with the default proportion of 2/3 to use in the tuning process below.