R: Set the hyperparameters of a learner object.

setHyperPars {mlr} | R Documentation

## Set the hyperparameters of a learner object.

### Description

Set the hyperparameters of a learner object.

### Usage

    setHyperPars(learner, ..., par.vals = list())

### Arguments

`learner` | (Learner | `character(1)`) The learner. If you pass a string the learner will be created via makeLearner.
`...` | (any) Optional named (hyper)parameters. If you want to set specific hyperparameters for a learner during model creation, these should go here. You can get a list of available hyperparameters using `getParamSet(<learner>)`. Alternatively hyperparameters can be given using the `par.vals` argument but `...` should be preferred!
`par.vals` | (list) Optional list of named (hyper)parameters. The arguments in `...` take precedence over values in this list. We strongly encourage you to use `...` for passing hyperparameters.

### Value

Learner.

### Note

If a named (hyper)parameter can't be found for the given learner, the 3 closest (hyper)parameter names will be output in case the user mistyped.

### See Also

Other learner: `LearnerProperties`, `getClassWeightParam()`, `getHyperPars()`, `getLearnerId()`, `getLearnerNote()`, `getLearnerPackages()`, `getLearnerParVals()`, `getLearnerParamSet()`, `getLearnerPredictType()`, `getLearnerShortName()`, `getLearnerType()`, `getParamSet()`, `helpLearnerParam()`, `helpLearner()`, `makeLearners()`, `makeLearner()`, `removeHyperPars()`, `setId()`, `setLearnerId()`, `setPredictThreshold()`, `setPredictType()`

### Examples

    cl1 = makeLearner("classif.ksvm", sigma = 1)
    cl2 = setHyperPars(cl1, sigma = 10, par.vals = list(C = 2))
    print(cl1)
    # note the now set and altered hyperparameters:
    print(cl2)

[Package mlr version 2.17.1 Index]