我发现了具有简单投票预测的成本敏感型OVO方案（https://mlr-org.github.io/mlr-tutorial/devel/html/cost_sensitive_classif/index.html）但是，有一种简单的方法可以跟随OVO方案，所有基本学习者都包含在MLR包中而没有成本矩阵和权重？
谢谢!!在@Lars回答之后编辑：
rm(list=ls(all=TRUE))
library(mlr)
df = iris
cost = matrix(runif(150 * 3, 0, 2000), 150) * (1 - diag(3))[df$Species,] + runif(150, 0, 10)
colnames(cost) = levels(iris$Species)
rownames(cost) = rownames(iris)
df$Species = NULL
costsens.task = makeCostSensTask(id = "iris", data = df, cost = cost)
costsens.task
lrn = makeLearner("classif.rotationForest")
lrn = makeCostSensWeightedPairsWrapper(lrn)
lrn
mod = train(lrn, costsens.task)
mod
getLearnerModel(mod)
pred = predict(mod, task = costsens.task)
pred
performance(pred, measures = list(meancosts, mcp), task = costsens.task)