在R中用训练数据集构建决策树后无法正常绘图的解决方案咨询

尝试使用如下训练数据集在R中创建决策树：

structure(list(Color = c(0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0), Size = c(1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0), Act = c(1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0), Age = c(1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0), Inflated = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,2L, 2L, 2L), levels = c("F", "T"), class = "factor")), class = "data.frame", row.names = c(NA, -22L))       

执行以下代码后：

Training1 <- as.data.frame(unclass(Training1), stringsAsFactors = TRUE)
tree1<- ctree(Inflated ~ ., Training1)

调用plot(tree1)无法生成正常的决策树图形，出现布局异常。

• 调整绘图参数：ctree默认绘图可能因窗口尺寸或布局设置导致显示异常，尝试指定简化布局或调整图形尺寸：

  # 使用simple类型简化树结构显示
  plot(tree1, type = "simple")
  
  # 若在脚本中运行，可指定输出图形尺寸避免压缩
  png("decision_tree.png", width = 1000, height = 800)
  plot(tree1, type = "simple")
  dev.off()
  

• 转换变量类型：你的输入特征（Color、Size、Act、Age）为数值型，但实际是二分类变量，转为因子类型后ctree的划分逻辑和绘图会更合理：

  # 将数值型分类变量转为因子
  Training1[, c("Color", "Size", "Act", "Age")] <- lapply(Training1[, c("Color", "Size", "Act", "Age")], as.factor)
  # 重新训练决策树
  tree1 <- ctree(Inflated ~ ., Training1)
  # 绘图
  plot(tree1)
  

• 使用替代绘图工具：如果party包的plot.ctree仍有问题，可切换到rpart包实现决策树，其绘图更稳定直观：

  library(rpart)
  library(rpart.plot)
  
  # 训练rpart决策树
  rpart_tree <- rpart(Inflated ~ ., data = Training1)
  # 生成清晰的决策树可视化
  rpart.plot(rpart_tree, box.palette = "GnBu", shadow.col = "gray", nn = TRUE)
  

内容的提问来源于stack exchange，提问作者ano273