R: Hyperparameter tuning for multiple measures at once.

tuneParamsMultiCrit {mlr} | R Documentation

## 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

    # 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)

[Package mlr version 2.14.0 Index]