# MeasureClassifCosts: Cost-sensitive Classification Measure In mllg/mlr3: Machine Learning in R - Next Generation

## Description

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. Costs are aggregated with the mean.

## Format

R6::R6Class() inheriting from MeasureClassif.

## Construction

 1 2 3 MeasureClassifCosts\$new(costs = NULL, normalize = TRUE) mlr_measures\$get("classif.costs") msr("classif.costs")
• 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.

Dictionary of Measures: mlr_measures

## Examples

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 # 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) # 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)

mllg/mlr3 documentation built on Sept. 27, 2019, 9:38 a.m.