标签: r-ranger

存在二元分类问题：如何获得 Ranger 模型变量的 Shap 贡献？

样本数据：

library(ranger)
library(tidyverse)

# Binary Dataset
df <- iris
df$Target <- if_else(df$Species == "setosa",1,0)
df$Species <- NULL

# Train Ranger Model
model <- ranger(
  x = df %>%  select(-Target),
  y = df %>%  pull(Target))

我尝试过几个库（DALEX、、、）shapr，但没有得到任何解决方案。fastshapshapper

SHAPforxgboost我希望得到像xgboost 这样的结果：

• 其输出shap.values是变量的形状贡献
• 这shap.plot.summary

尝试使用交叉折叠重采样并拟合 Ranger 包中的随机森林。无需重新采样的拟合工作正常，但一旦我尝试重新采样拟合，它就会失败并出现以下错误。

考虑以下df

df<-structure(list(a = c(1379405931, 732812609, 18614430, 1961678341, 
2362202769, 55687714, 72044715, 236503454, 61988734, 2524712675, 
98081131, 1366513385, 48203585, 697397991, 28132854), b = structure(c(1L, 
6L, 2L, 5L, 7L, 8L, 8L, 1L, 3L, 4L, 3L, 5L, 7L, 2L, 2L), .Label = c("CA", 
"IA", "IL", "LA", "MA", "MN", "TX", "WI"), class = "factor"), 
    c = structure(c(2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 
    2L, 2L, 2L, 1L), .Label = c("R", "U"), class = "factor"), 
    d = structure(c(3L, 3L, …