prune.tree 中的错误无法修剪 R.tree 中的单节点树

Posted 2023-03-12

【中文标题】prune.tree 中的错误无法修剪 R.tree 中的单节点树

【英文标题】：Error in prune.tree can not prune singlenode tree in R.tree

【发布时间】：2021-12-31 02:04:06

【问题描述】：

原始数据集是 7499 obs。 19 个变量。我在 R 中使用 tree 包来构建分类树。结果似乎合理，情节成功显示如下：

library(tree)
tree.data = tree(Y~., data.train, control = tree.control(dim(data)[1], mincut = 10, minsize = 20, mindev = 0.001))
plot(tree.data)
text(tree.data, pretty = 0,cex=0.6)

但是，当我尝试使用 cv.tree 修剪树时，出现错误。

cv.data = cv.tree(tree.data, FUN = prune.misclass)
Error in prune.tree(tree = list(frame = list(var = 1L, n = 6732, dev = 9089.97487458261,  : 
  can not prune singlenode tree

然后我检查tree.data 结构。

summary(tree.data)

Classification tree:
tree(formula = Y ~ ., data = data.train, control = tree.control(dim(data)[1], 
    mincut = 10, minsize = 20, mindev = 0.001))
Variables actually used in tree construction:
 [1] "X2"  "X1"  "X6"  "X13" "X5"  "X10" "X14" "X16" "X17" "X3"  "X7"  "X15" "X11" "X18"
[15] "X8"  "X12"
Number of terminal nodes:  45 
Residual mean deviance:  1.24 = 9243 / 7454 
Misclassification error rate: 0.3475 = 2606 / 7499 

这不是单节点树。所以我很困惑为什么会出现这个错误？

【参考方案1】：

这个错误是由cv.tree 在树被完全修剪并且只剩下根节点时产生的。当生成一组与Y 不相关的X 变量时，我可以重现您的错误。

library(tree)

# Data generating process
# Y is NOT associated to any X variables
set.seed(1234)
X <- matrix(rnorm(7499*18), ncol=18)
Y <- rbinom(7499, 1, 0.5)
data <- data.frame(Y=factor(Y, labels=c("No","Yes")), X)
idx <- sample(1:nrow(data), 6000)
data.train <- data[idx,]   
# Train the tree
tree.data = tree(Y~., data.train, 
            control=tree.control(dim(data)[1], mincut = 10, minsize = 20, mindev = 0.001))
plot(tree.data)
text(tree.data, pretty = 0,cex=0.6)    
# Pruning by cv.tree 
cv.data = cv.tree(tree.data, FUN = prune.misclass)

错误信息是：

prune.tree(tree = list(frame = list(var = 1L, n = 4842, dev = 6712.03745626047, : 不能修剪单节点树

现在假设 X1 与 Y 相关联。

# Data generating process
set.seed(1234)
X <- matrix(rnorm(7499*18), ncol=18)
Y <- X[,1]>0 + rbinom(7499, 1, 0.2)
data <- data.frame(Y=factor(Y, labels=c("No","Yes")), X)
idx <- sample(1:nrow(data), 6000)
data.train <- data[idx,]

cv.tree 命令现在不会抛出错误：

# Pruning by cv.tree
cv.data = cv.tree(tree.data, FUN = prune.misclass)
pruned.tree <- prune.tree(tree.data, k=cv.data$k[3])
plot(pruned.tree)
text(pruned.tree, pretty = 0, cex=0.6)

【讨论】：

非常感谢！！！！我一直在寻找几天来找出原因。你的回答完全解决了我的问题。