# Hyperparameter Optimization
# load learner and set search space
learner = lrn("classif.rpart",
cp = to_tune(1e-04, 1e-1),
minsplit = to_tune(2, 128),
minbucket = to_tune(1, 64)
)
# create design
design = mlr3misc::rowwise_table(
~cp, ~minsplit, ~minbucket,
0.1, 2, 64,
0.01, 64, 32,
0.001, 128, 1
)
# run hyperparameter tuning on the Palmer Penguins data set
instance = tune(
tuner = tnr("design_points", design = design),
task = tsk("penguins"),
learner = learner,
resampling = rsmp("holdout"),
measure = msr("classif.ce")
)
# best performing hyperparameter configuration
instance$result
# all evaluated hyperparameter configuration
as.data.table(instance$archive)
# fit final model on complete data set
learner$param_set$values = instance$result_learner_param_vals
learner$train(tsk("penguins"))
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