set.seed(31415L)
lrns = list(
lrn("classif.rpart", id = "rpart_md1", maxdepth = 1, predict_type = "prob"),
lrn("classif.rpart", id = "rpart_md5", maxdepth = 5, predict_type = "prob"),
lrn("classif.rpart", id = "rpart_md20", maxdepth = 20, predict_type = "prob"),
lrn("classif.ranger", id = "rf_mtryr0.2", mtry.ratio = 0.2, predict_type = "prob"),
lrn("classif.ranger", id = "rf_mtryr0.5", mtry.ratio = 0.5, predict_type = "prob"),
lrn("classif.ranger", id = "rf_mtry0.8", mtry.ratio = 0.8, predict_type = "prob"))
cv5 = rsmp("cv", folds = 5)
cv5$instantiate(task)
bmr = benchmark(benchmark_grid(task, lrns, cv5))
INFO (19:03:34.711) (mlr3) Running benchmark with 30 resampling iterations INFO (19:03:34.784) (mlr3) Applying learner 'rpart_md1' on task 'german_credit' (iter 1/5) INFO (19:03:34.816) (mlr3) Applying learner 'rpart_md1' on task 'german_credit' (iter 2/5) INFO (19:03:34.847) (mlr3) Applying learner 'rpart_md1' on task 'german_credit' (iter 3/5) INFO (19:03:34.878) (mlr3) Applying learner 'rpart_md1' on task 'german_credit' (iter 4/5) INFO (19:03:34.910) (mlr3) Applying learner 'rpart_md1' on task 'german_credit' (iter 5/5) INFO (19:03:34.940) (mlr3) Applying learner 'rpart_md5' on task 'german_credit' (iter 1/5) INFO (19:03:34.971) (mlr3) Applying learner 'rpart_md5' on task 'german_credit' (iter 2/5) INFO (19:03:35.009) (mlr3) Applying learner 'rpart_md5' on task 'german_credit' (iter 3/5) INFO (19:03:35.041) (mlr3) Applying learner 'rpart_md5' on task 'german_credit' (iter 4/5) INFO (19:03:35.072) (mlr3) Applying learner 'rpart_md5' on task 'german_credit' (iter 5/5) INFO (19:03:35.073) (mlr3) Applying learner 'rpart_md20' on task 'german_credit' (iter 1/5) INFO (19:03:35.119) (mlr3) Applying learner 'rpart_md20' on task 'german_credit' (iter 2/5) INFO (19:03:35.160) (mlr3) Applying learner 'rpart_md20' on task 'german_credit' (iter 3/5) INFO (19:03:35.202) (mlr3) Applying learner 'rpart_md20' on task 'german_credit' (iter 4/5) INFO (19:03:35.244) (mlr3) Applying learner 'rpart_md20' on task 'german_credit' (iter 5/5) INFO (19:03:35.286) (mlr3) Applying learner 'rf_mtryr0.2' on task 'german_credit' (iter 1/5) INFO (19:03:35.330) (mlr3) Applying learner 'rf_mtryr0.2' on task 'german_credit' (iter 2/5) INFO (19:03:35.373) (mlr3) Applying learner 'rf_mtryr0.2' on task 'german_credit' (iter 3/5) INFO (19:03:35.422) (mlr3) Applying learner 'rf_mtryr0.2' on task 'german_credit' (iter 4/5) INFO (19:03:35.467) (mlr3) Applying learner 'rf_mtryr0.2' on task 'german_credit' (iter 5/5) INFO (19:03:35.518) (mlr3) Applying learner 'rf_mtryr0.5' on task 'german_credit' (iter 1/5) INFO (19:03:35.571) (mlr3) Applying learner 'rf_mtryr0.5' on task 'german_credit' (iter 2/5) INFO (19:03:35.626) (mlr3) Applying learner 'rf_mtryr0.5' on task 'german_credit' (iter 3/5) INFO (19:03:35.683) (mlr3) Applying learner 'rf_mtryr0.5' on task 'german_credit' (iter 4/5) INFO (19:03:35.738) (mlr3) Applying learner 'rf_mtryr0.5' on task 'german_credit' (iter 5/5) INFO (19:03:35.794) (mlr3) Applying learner 'rf_mtry0.8' on task 'german_credit' (iter 1/5) INFO (19:03:35.850) (mlr3) Applying learner 'rf_mtry0.8' on task 'german_credit' (iter 2/5) INFO (19:03:35.902) (mlr3) Applying learner 'rf_mtry0.8' on task 'german_credit' (iter 3/5) INFO (19:03:35.955) (mlr3) Applying learner 'rf_mtry0.8' on task 'german_credit' (iter 4/5) INFO (19:03:36.014) (mlr3) Applying learner 'rf_mtry0.8' on task 'german_credit' (iter 5/5) INFO (19:03:36.324) (mlr3) Finished benchmark
mlr3viz::autoplot(bmr, measure = msr("classif.ce"))
Privind la Boxplots dezvăluie că performanța cursanților depinde foarte mult de alegerea hiperparametrelor.
Urmăriți întrebarea: Cum să setați corect hiperparametrele? Răspuns: Optimizarea hiperparameterului (a se vedea cazul de utilizare următor)
