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)
This exercise is part of the course
Hyperparameter Tuning in R
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create holdout sampling
holdout <- makeResampleDesc(___)
# Perform tuning
lrn_tune <- tuneParams(learner = lrn, task = task, resampling = holdout, control = ctrl_random, par.set = param_set)