r - 使用自定义指标的标准偏差选择带有插入符号的调整参数

Question

我正在使用带有自定义拟合指标的插入符号，但我不仅需要最大化这个指标，还需要最大化它的置信区间的下限。所以我想最大化像mean(metric) - k * stddev(metric). 我知道如何手动执行此操作，但有没有办法告诉插入符号使用此功能自动选择最佳参数？

Answer 1

是的，您可以通过“trainControl”对象的“summaryFunction”参数定义自己的选择指标，然后使用调用的“metric”参数定义自己的选择指标train()。这方面的详细信息在插入符号模型调整页面的“替代性能指标”部分中有很好的记录：http: //caret.r-forge.r-project.org/training.html

我认为您没有为任何人提供足够的信息来准确编写您要查找的内容，但这里有一个使用 twoClassSummary 函数中的代码的示例：

> library(caret)
> data(Titanic)
> 
> #an example custom function
> roc <- function (data, lev = NULL, model = NULL) {
+   require(pROC)
+   if (!all(levels(data[, "pred"]) == levels(data[, "obs"]))) 
+     stop("levels of observed and predicted data do not match")
+   rocObject <- try(pROC:::roc(data$obs, data[, lev[1]]), silent = TRUE)
+   rocAUC <- if (class(rocObject)[1] == "try-error") 
+     NA
+   else rocObject$auc
+   out <- c(rocAUC, sensitivity(data[, "pred"], data[, "obs"], lev[1]), specificity(data[, "pred"], data[, "obs"], lev[2]))
+   names(out) <- c("ROC", "Sens", "Spec")
+   out
+ }
> 
> #your train control specs
> tc <- trainControl(method="cv",classProb=TRUE,summaryFunction=roc)
> #yoru model with selection metric specificed
> train(Survived~.,data=data.frame(Titanic),method="rf",trControl=tc,metric="ROC")
32 samples
 4 predictors
 2 classes: 'No', 'Yes' 

No pre-processing
Resampling: Cross-Validation (10 fold) 

Summary of sample sizes: 28, 29, 30, 30, 28, 28, ... 

Resampling results across tuning parameters:

  mtry  ROC    Sens  Spec  ROC SD  Sens SD  Spec SD
  2     0.9    0.2   0.25  0.175   0.35     0.425  
  4     0.85   0.4   0.6   0.211   0.459    0.459  
  6     0.875  0.35  0.6   0.212   0.412    0.459  

ROC was used to select the optimal model using  the largest value.
The final value used for the model was mtry = 2. 

Answer 2

插入符号的火车功能帮助中有更多基本示例：

madSummary <- function (data,
                        lev = NULL,
                        model = NULL) {
  out <- mad(data$obs - data$pred, 
             na.rm = TRUE)  
  names(out) <- "MAD"
  out
}

robustControl <- trainControl(summaryFunction = madSummary)
marsGrid <- expand.grid(degree = 1, nprune = (1:10) * 2)

earthFit <- train(medv ~ .,
                  data = BostonHousing, 
                  method = "earth",
                  tuneGrid = marsGrid,
                  metric = "MAD",
                  maximize = FALSE,
                  trControl = robustControl)