For a classification learner the predict.type can be set to
“prob” to predict probabilities and the maximum value selects the
label. The threshold used to assign the label can later be changed using the
setThreshold function.
To see all possible properties of a learner, go to: LearnerProperties.
makeLearner(cl, id = cl, predict.type = "response",
predict.threshold = NULL, fix.factors.prediction = FALSE, ...,
par.vals = list(), config = list())(character(1))
Class of learner. By convention, all classification learners
start with “classif.” all regression learners with
“regr.” all survival learners start with “surv.”
all clustering learners with “cluster.” and all multilabel
classification learners start with “multilabel.”.
A list of all integrated learners is available on the
learners help page.
(character(1)) Id string for object. Used to display object.
Default is cl.
(character(1)) Classification: “response” (=
labels) or “prob” (= probabilities and labels by selecting the ones
with maximal probability). Regression: “response” (= mean response)
or “se” (= standard errors and mean response). Survival:
“response” (= some sort of orderable risk) or “prob” (= time
dependent probabilities). Clustering: “response” (= cluster IDS) or
“prob” (= fuzzy cluster membership probabilities), Multilabel:
“response” (= logical matrix indicating the predicted class labels)
or “prob” (= probabilities and corresponding logical matrix
indicating class labels). Default is “response”.
(numeric)
Threshold to produce class labels. Has to be a named vector, where names correspond to class labels.
Only for binary classification it can be a single numerical threshold for the positive class.
See setThreshold for details on how it is applied.
Default is NULL which means 0.5 / an equal threshold for each class.
(logical(1)) In some cases, problems occur
in underlying learners for factor features during prediction. If the new
features have LESS factor levels than during training (a strict subset),
the learner might produce an error like “type of predictors in new
data do not match that of the training data”. In this case one can repair
this problem by setting this option to TRUE. We will simply add the
missing factor levels missing from the test feature (but present in
training) to that feature. Default is FALSE.
(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!
(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.
(named list) Named list of config option to overwrite global settings set via configureMlr for this specific learner.
(Learner).
The former aims at specifying default hyperparameter settings from mlr
which differ from the actual defaults in the underlying learner. For
example, respect.unordered.factors is set to order in mlr while the
default in ranger::ranger depends on the argument splitrule.
getHyperPars(<learner>) can be used to query hyperparameter defaults that
differ from the underlying learner. This function also shows all
hyperparameters set by the user during learner creation (if these differ
from the learner defaults).
Other learner: LearnerProperties,
getClassWeightParam,
getHyperPars, getLearnerId,
getLearnerNote,
getLearnerPackages,
getLearnerParVals,
getLearnerParamSet,
getLearnerPredictType,
getLearnerShortName,
getLearnerType, getParamSet,
helpLearnerParam,
helpLearner, makeLearners,
removeHyperPars,
setHyperPars, setId,
setLearnerId,
setPredictThreshold,
setPredictType
# NOT RUN {
makeLearner("classif.rpart")
makeLearner("classif.lda", predict.type = "prob")
lrn = makeLearner("classif.lda", method = "t", nu = 10)
getHyperPars(lrn)
# }
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