Uses a cost matrix to create a classification measure. The cost matrix is stored as slot "costs". Costs are aggregated with the mean.

MeasureClassifCosts

## Format

R6::R6Class() inheriting from MeasureClassif.

MeasureClassifCosts$new(costs = NULL, normalize = TRUE)  • id :: character(1) Identifier for the measure. • costs :: matrix() Numeric matrix of costs (truth in columns, predicted response in rows). • normalize :: logical(1) If TRUE, calculate the mean costs instead of the total costs. ## Examples # get a cost sensitive task task = mlr_tasks$get("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
m = MeasureClassifCosts$new(id = "german_credit_costs", costs, normalize = FALSE) # fit models and calculate costs rr = resample(task, "classif.rpart", "cv3") rr$aggregate(m)#> german_credit_costs
#>            329.3333