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我正在尝试在 tidymodels 框架内构建一个 catboost 模型。下面给出了最小的可重现示例。我可以使用DALEX和modelStudio包来获得模型解释，但我想为这个 catboost 模型创建像这样的 VIP 图和像这样的摘要 shap 图。我已经尝试过类似的软件包，但没有任何运气。我意识到我必须从对象中提取变量重要性和形状值并使用它们来生成这些图，但不知道该怎么做。有没有办法在 R 中完成这项工作？fastshapSHAPforxgboostmodel

library(tidymodels)
library(treesnip)
library(catboost)
library(modelStudio)
library(DALEXtra)
library(DALEX)

data <- structure(list(Age = c(74, 60, 57, 53, 72, 72, 71, 77, 50, 66), StatusofNation0developed = structure(c(2L, 2L, 2L, 2L, 2L, 
                                                                                                       1L, 2L, 1L, 1L, 2L), .Label = c("0", "1"), class = "factor"), 
               treatment = structure(c(2L, 1L, 2L, 2L, 2L, 1L, 1L, 3L, 1L, 
                                       2L), .Label = c("0", "1", "2"), class = "factor"), InHospitalMortalityMortality = c(0, 
                                                                                                                           0, 1, 1, 1, 0, 0, 1, 1, 0)), row.names = c(NA, 10L), class = "data.frame")
split <- initial_split(data, strata = InHospitalMortalityMortality)
train <- training(split)
test <- testing(split)

train$InHospitalMortalityMortality <- as.factor(train$InHospitalMortalityMortality)

rec <- recipe(InHospitalMortalityMortality ~ ., data = train)

clf <- boost_tree() %>%
  set_engine("catboost") %>%
  set_mode("classification")

wflow <- workflow() %>%
  add_recipe(rec) %>%
  add_model(clf)

model <- wflow %>% fit(data = train)

explainer <- explain_tidymodels(model,
                                data = test,
                                y = test$InHospitalMortalityMortality,
                                label = "catboost")
new_observation <- test[1:2,]
modelStudio(explainer, new_observation)
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