我'm wondering if there is a way to specify which class of the outcome variable is positive in caret' s train() 功能 . 一个最小的例子：
# Settings
ctrl <- trainControl(method = "repeatedcv", number = 10, savePredictions = TRUE, summaryFunction = twoClassSummary, classProbs = TRUE)
# Data
data <- mtcars %>% mutate(am = factor(am, levels = c(0,1), labels = c("automatic", "manual"), ordered = T))
# Train
set.seed(123)
model1 <- train(am ~ disp + wt, data = data, method = "glm", family = "binomial", trControl = ctrl, tuneLength = 5)
# Data (factor ordering switched)
data <- mtcars %>% mutate(am = factor(am, levels = c(1,0), labels = c("manual", "automatic"), ordered = T))
# Train
set.seed(123)
model2 <- train(am ~ disp + wt, data = data, method = "glm", family = "binomial", trControl = ctrl, tuneLength = 5)
# Specifity and Sensitivity is switched
model1
model2
如果您运行代码，您会注意到两个模型中的特异性和敏感度指标都是"switched" . 看起来 train() 函数将因子结果变量的第一级作为正结果 . 有没有办法在函数本身中指定一个正类，所以无论结果因子排序如何，我都会得到相同的结果？我尝试添加 positive = "manual" ，但这会导致错误 .