library(dplyr)
# Choose model tuning parameters that minimize the number of predictors used
# while maximizing the area under the ROC curve.
classification_results |>
mutate(
d_feat = d_min(num_features, 1, 200),
d_roc = d_max(roc_auc, 0.5, 0.9),
d_all = d_overall(across(starts_with("d_")))
) |>
arrange(desc(d_all))
# Bias the ranking toward minimizing features by using a larger scale.
classification_results |>
mutate(
d_feat = d_min(num_features, 1, 200, scale = 3),
d_roc = d_max(roc_auc, 0.5, 0.9),
d_all = d_overall(across(starts_with("d_")))
) |>
arrange(desc(d_all))
Run the code above in your browser using DataLab