tuneParamsMultiCrit: Hyperparameter tuning for multiple measures at once.
In guillermozbta/mir: Machine Learning in R

Description Usage Arguments Value See Also Examples

Optimizes the hyperparameters of a learner in a multi-criteria fashion. Allows for different optimization methods, such as grid search, evolutionary strategies, etc. You can select such an algorithm (and its settings) by passing a corresponding control object. For a complete list of implemented algorithms look at TuneMultiCritControl.

1 2	tuneParamsMultiCrit(learner, task, resampling, measures, par.set, control, show.info = getMlrOption("show.info"), resample.fun = resample)

`learner`	[`Learner` \| `character(1)`] The learner. If you pass a string the learner will be created via `makeLearner`.
`task`	[`Task`] The task.
`resampling`	[`ResampleInstance` \| `ResampleDesc`] Resampling strategy to evaluate points in hyperparameter space. If you pass a description, it is instantiated once at the beginning by default, so all points are evaluated on the same training/test sets. If you want to change that behavior, look at `TuneMultiCritControl`.
`measures`	[list of `Measure`] Performance measures to optimize simultaneously.
`par.set`	[`ParamSet`] Collection of parameters and their constraints for optimization. Dependent parameters with a `requires` field must use `quote` and not `expression` to define it.
`control`	[`TuneMultiCritControl`] Control object for search method. Also selects the optimization algorithm for tuning.
`show.info`	[`logical(1)`] Print verbose output on console? Default is set via `configureMlr`.
`resample.fun`	[`closure`] The function to use for resampling. Defaults to `resample` and should take the same arguments as, and return the same result type as, `resample`.

[TuneMultiCritResult].

Other tune_multicrit: TuneMultiCritControl, plotTuneMultiCritResultGGVIS, plotTuneMultiCritResult

# multi-criteria optimization of (tpr, fpr) with NGSA-II
lrn =  makeLearner("classif.ksvm")
rdesc = makeResampleDesc("Holdout")
ps = makeParamSet(
  makeNumericParam("C", lower = -12, upper = 12, trafo = function(x) 2^x),
  makeNumericParam("sigma", lower = -12, upper = 12, trafo = function(x) 2^x)
)
ctrl = makeTuneMultiCritControlNSGA2(popsize = 4L, generations = 1L)
res = tuneParamsMultiCrit(lrn, sonar.task, rdesc, par.set = ps,
  measures = list(tpr, fpr), control = ctrl)
plotTuneMultiCritResult(res, path = TRUE)