我正在尝试使用xgbTree在Caret中仅使用3个变量训练回归模型。该模型仅预测2个离散值（2.3和2.6），这两个值都在模型数据范围之外（5个值介于2.8和4之间）。
此外，当我尝试计算变量重要性时，我收到以下警告消息
在FUN（newX [，i]，...）中：max没有非缺失参数;返回-Inf
仅列出1个变量，其重要性为NaN。
有人能够在这里解释我做错了什么吗？我附上了一个简单的示例代码来描述我的意思。
library("caret")
library("xgboost")
library("plyr")
x_linear<- c(
3.76, 3.88, 3.55, 3.47, 3.49, 3.44, 3.47, 3.49, 3.44, 3.92, 3.94, 3.55,
3.61, 3.57, 3.46, 3.72, 3.96, 3.55, 3.34, 3.26, 3.46, 3.69, 3.69, 3.53,
3.85, 3.78, 3.55, 3.42, 3.36, 3.53, 3.98, 3.91, 3.55, 3.95, 3.82, 3.55,
3.07, 3.02, 3.44, 3.45, 3.24, 3.46, 3.83, 3.90, 3.55, 3.89, 3.96, 3.55,
3.84, 3.76, 3.55, 3.78, 3.94, 3.46, 3.28, 3.47, 3.44, 3.66, 3.66, 3.46,
3.81, 3.90, 3.46, 3.66, 3.64, 3.46, 3.70, 3.69, 3.53, 3.89, 3.85, 3.55,
3.89, 3.85, 3.55, 3.39, 3.50, 3.46, 3.59, 3.52, 3.55, 3.42, 3.29, 3.44,
3.28, 3.39, 3.46, 3.23, 3.17, 3.53, 3.57, 3.28, 3.46, 3.61, 3.60, 3.46,
3.08, 3.02, 3.44, 3.55, 3.63, 3.55, 3.60, 3.63, 3.55, 3.26, 3.27, 3.53,
3.26, 3.27, 3.53, 3.54, 3.62, 3.46, 3.54, 3.62, 3.55, 3.29, 3.30, 3.44,
3.60, 3.59, 3.55, 4.00, 3.98, 3.55, 3.25, 3.41, 3.55, 3.59, 3.66, 3.55,
3.47, 3.51, 3.46)
x <- matrix(x_linear, nrow = 45, ncol = 3, byrow = TRUE)
colnames(x) <- c("x1","x2","x3")
y <- c(4.0, 3.7, 3.4, 4.0, 4.0, 4.0, 3.1, 3.4, 4.0, 3.4, 4.0, 4.0,
2.8, 3.4, 4.0, 4.0, 4.0, 4.0, 3.4, 3.7, 4.0, 3.7, 4.0, 4.0, 4.0,
3.4, 3.7, 3.4, 3.1, 3.1, 2.8, 3.4, 2.8, 3.7, 3.4, 3.1, 3.1, 4.0,
3.7, 3.1, 3.7, 4.0, 2.8, 3.7, 3.7)
trainControl <- trainControl(summaryFunction=defaultSummary,
method="repeatedcv", repeats=3, number=8, selectionFunction="oneSE")
tuneGrid <- expand.grid(.nrounds = 100, .max_depth = 6, .eta = 0.01,
.gamma = 0, .colsample_bytree = 1, .min_child_weight= 10)
#split into training and test sets
set.seed(1)
nSamples <- length(y)
TrainingIndexes <- createDataPartition(y, p=0.8)[[1]]
TestIndexes <- (seq(1,nSamples))[-TrainingIndexes]
# Preprocess data
procValues <- preProcess(x[TrainingIndexes,], method=c("center", "scale"))
ProcedData <- predict(procValues, x)
ProcedTrainingData <- ProcedData[TrainingIndexes,]
set.seed(1)
fit.xgb <- train(x=ProcedTrainingData, y=y[TrainingIndexes],
method="xgbTree",tuneGrid=tuneGrid,metric="RMSE", trControl=trainControl)
Imps <- varImp(fit.xgb)
Pred_y <- predict(fit.xgb, ProcedData)
plot(y[TrainingIndexes], Pred_y[TrainingIndexes],col="blue",
xlab="Meas", ylab="Pred")
points(y[TestIndexes], Pred_y[TestIndexes],col="red")