mlr is designed to make usage errors due to typos or invalid parameter values as unlikely as possible. Occasionally, you might want to break those barriers and get full access, for example to reduce the amount of output on the console or to turn off checks. For all available options simply refer to the documentation of configureMlr(). In the following we show some common use cases.

Generally, function configureMlr() permits to set options globally for your current R session.

It is also possible to set options locally.

Example: Reducing the output on the console

You are bothered by all the output on the console like in this example?

You can suppress the output for this Learner makeLearner() and this resample() call as follows:

lrn = makeLearner("classif.multinom", config = list(show.learner.output = FALSE))
r = resample(lrn, iris.task, rdesc, show.info = FALSE)

(Note that nnet::multinom() has a trace switch that can alternatively be used to turn off the progress messages.)

To globally suppress the output for all subsequent learners and calls to resample(), benchmark() etc. do the following:

configureMlr(show.learner.output = FALSE, show.info = FALSE)
r = resample("classif.multinom", iris.task, rdesc)

Example: Turning off parameter checking

It might happen that you want to set a parameter of a Learner (makeLearner(), but the parameter is not registered in the learner’s parameter set (ParamHelpers::makeParamSet()) yet. In this case you might want to contact us or open an issue as well! But until the problem is fixed you can turn off mlr’s parameter checking. The parameter setting will then be passed to the underlying function without further ado.

# Support Vector Machine with linear kernel and new parameter 'newParam'
lrn = makeLearner("classif.ksvm", kernel = "vanilladot", newParam = 3)
## Error in setHyperPars2.Learner(learner, insert(par.vals, args)): classif.ksvm: Setting parameter newParam without available description object!
## Did you mean one of these hyperparameters instead: degree scaled kernel
## You can switch off this check by using configureMlr!

# Turn off parameter checking completely
configureMlr(on.par.without.desc = "quiet")
lrn = makeLearner("classif.ksvm", kernel = "vanilladot", newParam = 3)
train(lrn, iris.task)
##  Setting default kernel parameters
## Model for learner.id=classif.ksvm; learner.class=classif.ksvm
## Trained on: task.id = iris-example; obs = 150; features = 4
## Hyperparameters: fit=FALSE,kernel=vanilladot,newParam=3

# Option "quiet" also masks typos
lrn = makeLearner("classif.ksvm", kernl = "vanilladot")
train(lrn, iris.task)
## Model for learner.id=classif.ksvm; learner.class=classif.ksvm
## Trained on: task.id = iris-example; obs = 150; features = 4
## Hyperparameters: fit=FALSE,kernl=vanilladot

# Alternatively turn off parameter checking, but still see warnings
configureMlr(on.par.without.desc = "warn")
lrn = makeLearner("classif.ksvm", kernl = "vanilladot", newParam = 3)
## Warning in setHyperPars2.Learner(learner, insert(par.vals, args)): classif.ksvm: Setting parameter kernl without available description object!
## Did you mean one of these hyperparameters instead: kernel nu degree
## You can switch off this check by using configureMlr!
## Warning in setHyperPars2.Learner(learner, insert(par.vals, args)): classif.ksvm: Setting parameter newParam without available description object!
## Did you mean one of these hyperparameters instead: degree scaled kernel
## You can switch off this check by using configureMlr!

train(lrn, iris.task)
## Model for learner.id=classif.ksvm; learner.class=classif.ksvm
## Trained on: task.id = iris-example; obs = 150; features = 4
## Hyperparameters: fit=FALSE,kernl=vanilladot,newParam=3

Example: Handling errors in a learning method

If a learning method throws an error the default behavior of mlr is to generate an exception as well. However, in some situations, for example if you conduct a larger bechmark experiment with multiple data sets and learners, you usually don’t want the whole experiment stopped due to one error. You can prevent this using the on.learner.error option of configureMlr().

If on.learner.error = "warn" a warning is issued instead of an exception and an object of class FailureModel() is created. You can extract the error message using function getFailureModelMsg(). All further steps like prediction and performance calculation work and return NA's.