If I pass k > 1 measures to the tuning instance, it gets automatically treated as a multi-objective problem by hyperband with k objectives.
We eventually want to optimize w.r.t. l < k measures only, but just log another measure.
Example: For hyperband, we might want to optimize for a performance measure, but log the training time for the configurations to see how the budget parameter influences our actual resource, the training time.
It is not possible with the actual mlr3tuning/bbotk design. However you can calculate most measures afterwards on the resample results that are stored in the archive.
If I pass k > 1 measures to the tuning instance, it gets automatically treated as a multi-objective problem by hyperband with k objectives.
We eventually want to optimize w.r.t. l < k measures only, but just log another measure.
Example: For hyperband, we might want to optimize for a performance measure, but log the training time for the configurations to see how the budget parameter influences our actual resource, the training time.