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 usinggetParamSet(<learner>). Alternatively hyperparameters can be given using thepar.valsargument 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.
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)
#> Learner classif.ksvm from package kernlab
#> Type: classif
#> Name: Support Vector Machines; Short name: ksvm
#> Class: classif.ksvm
#> Properties: twoclass,multiclass,numerics,factors,prob,class.weights
#> Predict-Type: response
#> Hyperparameters: fit=FALSE,sigma=1
#>
# note the now set and altered hyperparameters:
print(cl2)
#> Learner classif.ksvm from package kernlab
#> Type: classif
#> Name: Support Vector Machines; Short name: ksvm
#> Class: classif.ksvm
#> Properties: twoclass,multiclass,numerics,factors,prob,class.weights
#> Predict-Type: response
#> Hyperparameters: fit=FALSE,sigma=10,C=2
#>