R: Create learner object.

makeLearner {mlr} | R Documentation

## Create learner object.

### Description

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.

### Usage

    makeLearner(cl, id = cl, predict.type = "response",
      predict.threshold = NULL, fix.factors.prediction = FALSE, ...,
      par.vals = list(), config = list())

### Arguments

`cl` | (`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.
`id` | (`character(1)`) Id string for object. Used to display object. Default is `cl`.
`predict.type` | (`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”.
`predict.threshold` | (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.
`fix.factors.prediction` | (`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!
`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.
`config` | (named list) Named list of config option to overwrite global settings set via configureMlr for this specific learner.

### Value

(Learner).

### `par.vals` vs. `...`

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).

### See Also

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

### Examples

    makeLearner("classif.rpart")
    makeLearner("classif.lda", predict.type = "prob")
    lrn = makeLearner("classif.lda", method = "t", nu = 10)
    getHyperPars(lrn)

[Package mlr version 2.15.0 Index]