mlr3 (version 0.1.1)

MeasureClassifCosts: Cost-sensitive Classification Measure

Description

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

Usage

MeasureClassifCosts

Format

R6::R6Class() inheriting from MeasureClassif.

Construction

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

# NOT RUN {
# 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)

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

# create measure which calculates the absolute costs
m = MeasureClassifCosts$new(id = "german_credit_costs", costs, normalize = FALSE)

# fit models and calculate costs
rr = resample(task, "classif.rpart", "cv3")
rr$aggregate(m)
# }