mlr (version 2.13)

tuneParamsMultiCrit: Hyperparameter tuning for multiple measures at once.

Description

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].

Usage

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

Arguments

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

([ParamHelpers::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].

Value

([TuneMultiCritResult]).

See Also

Other tune_multicrit: TuneMultiCritControl, plotTuneMultiCritResult

Examples

# NOT RUN {
# 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)
# }