train.default(x, y, Weights = w, ...) 中的错误：无法确定最终调整参数

我对机器学习非常陌生，正在尝试Kaggle 上的森林覆盖预测竞赛，但我很早就挂断了。当我运行下面的代码时，出现以下错误。

Error in train.default(x, y, weights = w, ...) : 
final tuning parameters could not be determined
In addition: There were 50 or more warnings (use warnings() to see the first 50)
  

# Load the libraries
library(ggplot2); library(caret); library(AppliedPredictiveModeling)
library(pROC)
library(Amelia)

set.seed(1234)

# Load the forest cover dataset from the csv file
rawdata <- read.csv("train.csv",stringsAsFactors = F)
#this data won't be used in model evaluation. It will only be used for the submission.
test <- read.csv("test.csv",stringsAsFactors = F)

########################
### DATA PREPARATION ###
########################

#create a training and test set for building and evaluating the model
samples <- createDataPartition(rawdata$Cover_Type, p = 0.5,list = FALSE)
data.train <- rawdata[samples, ]
data.test <- rawdata[-samples, ]

model1 <- train(as.factor(Cover_Type) ~ Elevation + Aspect + Slope + Horizontal_Distance_To_Hydrology, 
                data = data.train, 
                method = "rf", prox = "TRUE")

以下应该有效：

model1 <- train(as.factor(Cover_Type) ~ Elevation + Aspect + Slope + Horizontal_Distance_To_Hydrology,
                          data = data.train,
                          method = "rf", tuneGrid = data.frame(mtry = 3))

最好指定tuneGrid参数是具有可能的调整值的数据帧。看着?randomForest and ?train了解更多信息。rf只有一个调整参数mtry，它控制为每棵树选择的特征数量。

你也可以运行modelLookup获取每个模型的调整参数列表

> modelLookup("rf")
#  model parameter                         label forReg forClass probModel
#1    rf      mtry #Randomly Selected Predictors   TRUE     TRUE      TRUE