This package provides hyperparameter tuning for mlr3. It offers various tuning methods e.g. grid search, random search and generalized simulated annealing and different termination criteria can be set and combined. ‘AutoTuner’ provides a convenient way to perform nested resampling in combination with ‘mlr3’. The package is build on bbotk which provides a common framework for optimization.
library("mlr3") library("mlr3tuning") library("paradox") task = tsk("pima") learner = lrn("classif.rpart") resampling = rsmp("holdout") measure = msr("classif.ce") # Create the search space with lower and upper bounds search_space = ParamSet$new(list( ParamDbl$new("cp", lower = 0.001, upper = 0.1), ParamInt$new("minsplit", lower = 1, upper = 10) )) # Define termination criterion terminator = trm("evals", n_evals = 20) # Create tuning instance instance = TuningInstanceSingleCrit$new(task = task, learner = learner, resampling = resampling, measure = measure, search_space = search_space, terminator = terminator) # Load tuner tuner = tnr("grid_search", resolution = 5) # Trigger optimization tuner$optimize(instance) # View results instance$result