在随机森林 mtry 图中添加点

Question

我正在使用带有 caret 包的随机森林来设置最佳 mtry（预测因子的数量）。当我绘制模型以查看 RMSE 随 mtry 的变化时，我想在最佳 mtry

中添加一个点

ctrl <- trainControl(method = "cv", savePred=T, number = 10)
tunegrid <- expand.grid(.mtry=seq(from=2,to=nlayers(covs_processed),by=2))
# Search for the best mtry parameter
rfmodel <- train(fm, data=dat_1963@data, method = "rf", trControl = ctrl,
                 importance=TRUE, tuneGrid=tunegrid)

plot(rfmodel,main= "Tuning RF 2018")

定位点：

rfmodel[11][[1]]$tuneValue[[1]]

24

min(rfmodel$results$RMSE)

2.972381

我尝试用这段代码添加点，但我可以

points(rfmodel[11][[1]]$tuneValue[[1]],min(rfmodel$results$RMSE),col="red")

模型可以在这里找到： https://drive.google.com/open?id=1tFFgxuCiJNC4PLMekBG7bgEziKGwMJmu

Answer 1

caret 中的 plot() 方法使用 lattice 包而不是基础图形，因此 lines 不起作用。

您可以通过添加新的绘图图层使用 ggplot 方法轻松获得结果。这里有两个选项：

library(caret)
#> Loading required package: lattice
#> Loading required package: ggplot2
data(BloodBrain)

theme_set(theme_bw())

ctrl <- trainControl(method = "cv", number = 10, returnResamp = "all")
set.seed(214)
rfmodel <-
  train(
    x = bbbDescr, y = logBBB,
    method = "rf",
    preProc = "zv",
    trControl = ctrl,
    tuneGrid = data.frame(mtry = 1:10)
  )

# rfmodel$resample contains the individual RMSE values per fold. Use the
# ggplot method and add another layer with those points. 

ggplot(rfmodel) + 
  geom_point(data = rfmodel$resample, alpha = .3)

# or add them as colored lines 
ggplot(rfmodel) + 
  geom_line(
    data = rfmodel$resample, 
    # For each resample, plot a different colored line
    aes(group = Resample, col = Resample),
    alpha = .3) + 
  # the legend here gets huge so stop it from being printed
  theme(legend.position = "none")

^{由 reprex package (v0.2.1)}

于 2019-04-03 创建

在随机森林 mtry 图中添加点

Add points in a Random Forest mtry plot

plot

r

random-forest

r-caret