Cost-sensitive Classification Measure
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 $costs.
For calculation of the score, the confusion matrix is multiplied element-wise with the cost matrix.
The costs are then summed up (and potentially divided by the number of observations if normalize is set to TRUE).
This measure requires the Task during scoring to ensure that the rows and columns of the cost matrix are in the same order as in the confusion matrix.
This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():
mlr_measures$get("classif.costs")
msr("classif.costs") Type: "classif"
Range: [0, Inf)
Minimize: TRUE
Required prediction: 'response'
mlr3::Measure -> mlr3::MeasureClassif -> MeasureClassifCosts
normalize(logical(1))
Normalize the costs?
costs(numeric matrix())
Matrix of costs (truth in columns, predicted response in rows).
new()
Creates a new instance of this R6 class.
MeasureClassifCosts$new()
clone()
The objects of this class are cloneable with this method.
MeasureClassifCosts$clone(deep = FALSE)
deepWhether to make a deep clone.
as.data.table(mlr_measures) for a complete table of all (also dynamically created) Measure implementations.
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.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.mbrier
# 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.