For this example, the restart was set to 10 iterations but it restarts at iteration 8:
ctrl_sa <- control_sim_anneal(verbose = TRUE, no_improve = 10L)
set.seed(1234)
svm_sa <-
svm_wflow %>%
tune_sim_anneal(
resamples = penguins_folds,
metrics = roc_res,
initial = svm_initial,
param_info = svm_param,
iter = 50,
control = ctrl_sa
)
## Optimizing roc_auc
## Initial best: 0.84948
## 1 ◯ accept suboptimal roc_auc=0.57004 (+/-0.172)
## 2 ◯ accept suboptimal roc_auc=0.57004 (+/-0.172)
## 3 ◯ accept suboptimal roc_auc=0.56876 (+/-0.1715)
## 4 ◯ accept suboptimal roc_auc=0.56876 (+/-0.1715)
## 5 ◯ accept suboptimal roc_auc=0.56876 (+/-0.1715)
## 6 + better suboptimal roc_auc=0.56942 (+/-0.1715)
## 7 ◯ accept suboptimal roc_auc=0.56848 (+/-0.1718)
## 8 ✖ restart from best roc_auc=0.56876 (+/-0.1715)
## 9 ◯ accept suboptimal roc_auc=0.84948 (+/-0.01685)
## 10 ◯ accept suboptimal roc_auc=0.84948 (+/-0.01685)
Also, for some other reason, the initial grid gives different ROC results each run. This might be due to the Platt scaling used by kernlab; its CV doesn't use a controllable seed. However, we don't see this in the book example.
For this example, the restart was set to 10 iterations but it restarts at iteration 8:
Also, for some other reason, the initial grid gives different ROC results each run. This might be due to the Platt scaling used by
kernlab; its CV doesn't use a controllable seed. However, we don't see this in the book example.