编辑：正如在下面的评论中指出的那样，这给出了预测的置信区间，而不是严格的预测区间。对我的回复感到有点高兴，应该对此多加考虑。

随意忽略此答案或尝试构建代码以获取预测间隔。

我已经使用简单的引导程序创建了几次预测间隔，但可能还有其他（更好的）方法。

考虑包oil中的数据，caret假设我们想要生成部分依赖关系和 95% 的区间，以了解硬脂酸对 Palmitic 的影响。下面只是一个简单的示例，但您可以根据自己的需要随意使用它。确保gbm软件包已更新以允许grid.points参数plot.gbm

library(caret)
data(oil)
#train the gbm using just the defaults.
tr <- train(Palmitic ~ ., method = "gbm" ,data = fattyAcids, verbose = FALSE)

#Points to be used for prediction. Use the quartiles here just for illustration
x.pt <- quantile(fattyAcids$Stearic, c(0.25, 0.5, 0.75))

#Generate the predictions, or in this case, the partial dependencies at the selected points. Substitute plot() for predict() to get predictions
p <- plot(tr$finalModel, "Stearic", grid.levels = x.pt, return.grid = TRUE)

#Bootstrap the process to get prediction intervals
library(boot)

bootfun <- function(data, indices) {
  data <- data[indices,]

  #As before, just the defaults in this example. Palmitic is the first variable, hence data[,1]
  tr <- train(data[,-1], data[,1], method = "gbm", verbose=FALSE)

  # ... other steps, e.g. using the oneSE rule etc ...
  #Return partial dependencies (or predictions)

  plot(tr$finalModel, "Stearic", grid.levels = x.pt, return.grid = TRUE)$y
  #or predict(tr$finalModel, data = ...)
}

#Perform the bootstrap, this can be very time consuming. Just 99 replicates here but we usually want to do more, e.g. 500. Consider using the parallel option
b <- boot(data = fattyAcids, statistic = bootfun, R = 99)

#Get the 95% intervals from the boot object as the 2.5th and 97.5th percentiles
lims <- t(apply(b$t, 2, FUN = function(x) quantile(x, c(0.025, 0.975))))

这是做到这一点的一种方法，它至少试图解释因调整 gbm 而产生的不确定性。http://onlinelibrary.wiley.com/doi/10.2193/2006-503/abstract中使用了类似的方法

有时点估计在区间之外，但修改调整网格（即增加树的数量和/或深度）通常可以解决这个问题。

希望这可以帮助！