如何解决如何使用特异性和敏感性度量的总和作为 R 插入符中训练的汇总度量？
我在 R 中为 xgbtree 使用插入符号：
fitControl_2 <- trainControl(## 3-fold CV
method = "repeatedcv",number = 3,repeats = 2,verboseIter = TRUE,)
xgboost <- train(interest_factor ~ .,data = train_set_balanced,method = "xgbTree",trControl = fitControl_2,## Specify which metric to optimize
metric = "Kappa")
有没有办法使用 Sensitivity+Specificity 或 Youden Index 作为指标而不是 Kappa？我知道您可以使用自定义函数，但不清楚在这种情况下如何正确构建。
解决方法
这是一个汇总函数，它将使用 Sens + Spec 的总和作为选择指标：
youdenSumary <- function(data,lev = NULL,model = NULL){
if (length(lev) > 2) {
stop(paste("Your outcome has",length(lev),"levels. The joudenSumary() function isn't appropriate."))
}
if (!all(levels(data[,"pred"]) == lev)) {
stop("levels of observed and predicted data do not match")
}
Sens <- caret::sensitivity(data[,"pred"],data[,"obs"],lev[1])
Spec <- caret::specificity(data[,lev[2])
j <- Sens + Spec
out <- c(j,Spec,Sens)
names(out) <- c("j","Spec","Sens")
out
}
要了解为何如此定义，请阅读插入符号手册中的此 chapter。一些可能对 SO 有帮助的答案是：
Custom Performance Function in caret Package using predicted Probability
Additional metrics in caret - PPV,sensitivity,specificity
示例：
library(caret)
library(mlbench)
data(Sonar)
fitControl <- trainControl(method = "cv",number = 5,summaryFunction = youdenSumary)
fit <- train(Class ~.,data = Sonar,method = "rpart",metric = "j",tuneLength = 5,trControl = fitControl)
fit
#output
CART
208 samples
60 predictor
2 classes: 'M','R'
No pre-processing
Resampling: Cross-Validated (5 fold)
Summary of sample sizes: 167,166,167
Resampling results across tuning parameters:
cp j Spec Sens
0.00000000 1.394980 0.6100000 0.7849802
0.01030928 1.394980 0.6100000 0.7849802
0.05154639 1.387708 0.6300000 0.7577075
0.06701031 1.398629 0.6405263 0.7581028
0.48453608 1.215457 0.3684211 0.8470356
j was used to select the optimal model using the largest value.
The final value used for the model was cp = 0.06701031.
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