r - Caret 包中的用户定义指标

Question

我希望使用带有不是默认选项之一的指标的插入符号包。对于下面的示例，我使用了 Metrics 包。我已经阅读了 StackOverflow 上的所有相关问题以及 caret 网站上的指南，但仍然收到错误消息。

在下面的示例中，我希望使用平均绝对误差。

创建一个函数:

maefunction<-function(data, lev=NULL, model=NULL){
  require(Metrics)
  MAE<-mae(data[, "obs"], data[, "pred"])
  out<-c(MAE)
  out
}

现在我将函数插入 trainControl

library(caret)
GBM<-train(train$result~., data=train, method="gbm", trControl=trainControl(summaryFunction=maefunction), metric=MAE)

我收到以下消息

Error in list_to_dataframe(res, attr(.data, "split_labels"), .id, id_as_factor) : 
Results must be all atomic, or all data frames
In addition: Warning messages:
1: In if (metric %in% c("Accuracy", "Kappa")) stop(paste("Metric",  :
  the condition has length > 1 and only the first element will be used
2: In if (metric == "ROC" & !ctrl$classProbs) stop("train()'s use of ROC codes requires                class probabilities. See the classProbs option of trainControl()") :
  the condition has length > 1 and only the first element will be used
3: In if (!(metric %in% perfNames)) { :
  the condition has length > 1 and only the first element will be used
4: In train.default(x, y, weights = w, ...) :
  The metric "4" was not in the result set.  will be used instead.The metric "0.5" was    not in the result set.  will be used instead.

Answer 1

我认为您必须使用命名向量(请参见下面的示例)。我没有在文档中明确说明这一点，因此我将更新该部分。

最大限度

library(mlbench)
data(BostonHousing)

maeSummary <- function (data,
                        lev = NULL,
                        model = NULL) {
   out <- mae(data$obs, data$pred)  
   names(out) <- "MAE"
   out
}

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

set.seed(1)
earthFit <- train(medv ~ .,
                  data = BostonHousing, 
                  "earth",
                  tuneGrid = marsGrid,
                  metric = "MAE",
                  maximize = FALSE,
                  trControl = mControl)