Define aggregated measures

Now, you are going to define performance measures. 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.nnet", fix.factors.prediction = TRUE)

param_set <- makeParamSet(
  makeIntegerParam("size", lower = 1, upper = 5),
  makeIntegerParam("maxit", lower = 1, upper = 300),
  makeNumericParam("decay", lower = 0.0001, upper = 1)
)

ctrl_random <- makeTuneControlRandom(maxit = 10)

Use the setAggregation function, which aggregates the standard deviation of performance metrics.
Apply setAggregation to the mean misclassification error and accuracy after resampling.
Optimize your model by mean misclassification error. Remember that the first argument is used for optimization.

Exercise

Define aggregated measures

Instructions