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mlr-org / mlr3tuning

Hyperparameter optimization package of the mlr3 ecosystem
https://mlr3tuning.mlr-org.com/
GNU Lesser General Public License v3.0
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Tuning Sugar #293

Closed be-marc closed 3 years ago

be-marc commented 3 years ago

Closes #157 

learner = lrn("classif.rpart")
learner$param_set$values$minsplit = to_tune(1, 10)

parameters = tune(method = "random_search", task = tsk("pima"), learner = learner, 
  resampling = rsmp ("holdout"), measure = msr("classif.ce"), term_evals = 50, 
  batch_size = 10)   

learner$param_set$values = parameters
learner = lrn("classif.rpart")
learner$param_set$values$minsplit = to_tune(1, 10)

at = auto_tuner(method = "random_search", learner = learner, 
  resampling = rsmp ("holdout"),  measure = msr("classif.ce"), term_evals = 50,
  batch_size = 10)  

at$train(tsk("pima"))
learner = lrn("classif.rpart")
learner$param_set$values$minsplit = to_tune(1, 10)

rr = tune_nested(method = "random_search", task = tsk("pima"), learner = learner, 
  inner_resampling = rsmp ("holdout"), outer_resampling = rsmp("cv", folds = 3), 
  measure = msr("classif.ce"), term_evals = 50, batch_size = 10)