Perform hyperparameter tuning with mlr
Now, you can combine the prepared functions and objects from the previous exercise to actually perform hyperparameter tuning with random search.
The knowledge_train_data
dataset has already been loaded for you, as have the packages mlr
, tidyverse
and tictoc
. And the following code has also been run already:
# Define task
task <- makeClassifTask(data = knowledge_train_data,
target = "UNS")
# Define learner
lrn <- makeLearner("classif.nnet", predict.type = "prob", fix.factors.prediction = TRUE)
# Define set of parameters
param_set <- makeParamSet(
makeDiscreteParam("size", values = c(2,3,5)),
makeNumericParam("decay", lower = 0.0001, upper = 0.1)
)
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.
# Define a random search tuning method.
ctrl_random <- makeTuneControlRandom(___ = ___)