3.3 Learners

Objects of class mlr3::Learner provide a unified interface to many popular machine-learning algorithms in R. They consist of methods to train and predict on a mlr3::Task, and additionally provide meta information about the algorithms.

The package ships with only a rather minimal set of classification and regression learners, more are implemented in the mlr3learners package. Furthermore, mlr3learners has some documentation on creating custom learners.

3.3.1 Predefined Learners

Similar to mlr3::mlr_tasks, the mlr3misc::Dictionary mlr3::mlr_learners can be queried for available learners:

mlr_learners
## <DictionaryLearner> with 5 stored values
## Keys: classif.debug, classif.featureless, classif.rpart,
##   regr.featureless, regr.rpart
as.data.table(mlr_learners)
##                    key                                    feature_types
## 1:       classif.debug logical,integer,numeric,character,factor,ordered
## 2: classif.featureless logical,integer,numeric,character,factor,ordered
## 3:       classif.rpart logical,integer,numeric,character,factor,ordered
## 4:    regr.featureless logical,integer,numeric,character,factor,ordered
## 5:          regr.rpart logical,integer,numeric,character,factor,ordered
##    packages                                                        properties
## 1:                                               missings,multiclass,twoclass
## 2:                  importance,missings,multiclass,selected_features,twoclass
## 3:    rpart importance,missings,multiclass,selected_features,twoclass,weights
## 4:    stats                             importance,missings,selected_features
## 5:    rpart                     importance,missings,selected_features,weights
##    predict_types
## 1: response,prob
## 2: response,prob
## 3: response,prob
## 4:   response,se
## 5:      response

As listed in the output, each learner comes with the following information:

• feature_types: what kind of features can be processed.
• packages: which packages are required to run train() and predict().
• properties: additional properties and capabilities. For example, a learner has the property “missings” if it is able to handle missing values, and “importance” if it is possible to extract feature importance values.
• predict_types: what predict types are possible. For example, a classification learner can predict labels (“response”) or probabilities (“prob”).

To extract a specific learner, use the corresponding "id":

learner = mlr_learners$get("classif.rpart")
print(learner)
## <LearnerClassifRpart:classif.rpart>
## Model: -
## Parameters: xval=0
## Packages: rpart
## Predict Type: response
## Feature types: logical, integer, numeric, character, factor, ordered
## Properties: importance, missings, multiclass, selected_features,
##   twoclass, weights

In the output we see that all information from the previous table is also accessible via public slots (id, feature_types, packages, properties, predict_types). Additionally, predict_type returns the currently selected predict type of the learner.

Slot param_set stores a description of hyperparameter settings:

learner$param_set
## ParamSet: 
##              id    class lower upper levels default value
## 1:     minsplit ParamInt     1   Inf             20      
## 2:           cp ParamDbl     0     1           0.01      
## 3:   maxcompete ParamInt     0   Inf              4      
## 4: maxsurrogate ParamInt     0   Inf              5      
## 5:     maxdepth ParamInt     1    30             30      
## 6:         xval ParamInt     0   Inf             10     0

The set of hyperparameter values is stored inside the parameter set in the values slot. By assigning a named list to this slot, we change the active hyperparameters of the learner:

learner$param_set$values = list(cp = 0.01)
learner
## <LearnerClassifRpart:classif.rpart>
## Model: -
## Parameters: cp=0.01
## Packages: rpart
## Predict Type: response
## Feature types: logical, integer, numeric, character, factor, ordered
## Properties: importance, missings, multiclass, selected_features,
##   twoclass, weights

The slot model stores the result of the training step. As we have not yet trained a model, this slot is NULL:

learner$model
## NULL