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).

This measure requires the Task during scoring to ensure that the rows and columns of the cost matrix are in the same order as in the confusion matrix.


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


Meta Information

  • Type: "classif"

  • Range: \([0, \infty)\)

  • Minimize: TRUE

  • Required prediction: 'response'

See also

Super classes

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

Public fields


Normalize the costs?

Active bindings


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


Public methods

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) print(costs)
#> predicted #> truth good bad #> good 0 1 #> bad 5 0
# mlr3 needs truth in columns, predictions in rows costs = t(costs) # create measure which calculates the absolute costs m = msr("classif.costs", id = "german_credit_costs", costs = costs, normalize = FALSE) # fit models and calculate costs learner = lrn("classif.rpart") rr = resample(task, learner, rsmp("cv", folds = 3)) rr$aggregate(m)
#> german_credit_costs #> 305