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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)).

Dictionary

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

mlr_measures$get("classif.costs")
msr("classif.costs")

Meta Information

  • Task type: “classif”

  • Range: \((-\infty, \infty)\)

  • Minimize: TRUE

  • Average: macro

  • Required Prediction: “response”

  • Required Packages: mlr3

Parameters

IdTypeDefaultLevels
normalizelogical-TRUE, FALSE

See also

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

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.fnr, mlr_measures_classif.fn, mlr_measures_classif.fomr, mlr_measures_classif.fpr, mlr_measures_classif.fp, 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.tn, mlr_measures_classif.tpr, mlr_measures_classif.tp

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

costs

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

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


Method clone()

The objects of this class are cloneable with this method.

Usage

MeasureClassifCosts$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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

# cost matrix as given on the UCI page of the german credit data set
# https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data)
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 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))
rr$aggregate(m)
#> german_credit_costs 
#>            336.6667