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Uses a cost matrix to create a classification measure. True labels must be arranged in columns, predicted labels must be arranged in rows. The cost matrix is stored as slot $costs.

For calculation of the score, the confusion matrix is multiplied element-wise with the cost matrix. The costs are then summed up (and potentially divided by the number of observations if normalize is set to TRUE (default)).


This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():


Meta Information

  • Task type: “classif”

  • Range: \((-\infty, \infty)\)

  • Minimize: TRUE

  • Average: macro

  • Required Prediction: “response”

  • Required Packages: mlr3


normalizelogical-TRUE, FALSE

See also

Other Measure: Measure, MeasureClassif, MeasureRegr, MeasureSimilarity, mlr_measures, mlr_measures_aic, mlr_measures_bic, mlr_measures_debug_classif, mlr_measures_elapsed_time, mlr_measures_oob_error, mlr_measures_selected_features

Other classification measures: mlr_measures_classif.acc, mlr_measures_classif.auc, mlr_measures_classif.bacc, mlr_measures_classif.bbrier, mlr_measures_classif.ce, mlr_measures_classif.dor, mlr_measures_classif.fbeta, mlr_measures_classif.fdr, mlr_measures_classif.fn, mlr_measures_classif.fnr, mlr_measures_classif.fomr, mlr_measures_classif.fp, mlr_measures_classif.fpr, mlr_measures_classif.logloss, mlr_measures_classif.mauc_au1p, mlr_measures_classif.mauc_au1u, mlr_measures_classif.mauc_aunp, mlr_measures_classif.mauc_aunu, mlr_measures_classif.mbrier, mlr_measures_classif.mcc, mlr_measures_classif.npv, mlr_measures_classif.ppv, mlr_measures_classif.prauc, mlr_measures_classif.precision, mlr_measures_classif.recall, mlr_measures_classif.sensitivity, mlr_measures_classif.specificity,, mlr_measures_classif.tnr,, mlr_measures_classif.tpr

Other multiclass classification measures: mlr_measures_classif.acc, mlr_measures_classif.bacc, mlr_measures_classif.ce, mlr_measures_classif.logloss, mlr_measures_classif.mauc_au1p, mlr_measures_classif.mauc_au1u, mlr_measures_classif.mauc_aunp, mlr_measures_classif.mauc_aunu, mlr_measures_classif.mbrier

Super classes

mlr3::Measure -> mlr3::MeasureClassif -> MeasureClassifCosts

Active bindings


(numeric matrix())
Matrix of costs (truth in columns, predicted response in rows).


Inherited methods

Method new()

Creates a new instance of this R6 class.


Method clone()

The objects of this class are cloneable with this method.


MeasureClassifCosts$clone(deep = FALSE)



Whether to make a deep clone.


# get a cost sensitive task
task = tsk("german_credit")

# cost matrix as given on the UCI page of the german credit data set
costs = matrix(c(0, 5, 1, 0), nrow = 2)
dimnames(costs) = list(truth = task$class_names, predicted = task$class_names)
#>       predicted
#> truth  good bad
#>   good    0   1
#>   bad     5   0

# mlr3 needs truth in columns, predictions in rows
costs = t(costs)

# create a cost measure which calculates the absolute costs
m = msr("classif.costs", id = "german_credit_costs", costs = costs, normalize = FALSE)

# fit models and evaluate with the cost measure
learner = lrn("classif.rpart")
rr = resample(task, learner, rsmp("cv", folds = 3))
#> german_credit_costs 
#>            320.6667