R 带有tidyModel框架的支持向量机的变量重要性图不起作用

必须更新DR pred_wrapper以返回向量，而不是数据帧. 不应该为了获得有意义的结果而对变量进行标准化. pull_workflow_fit被替换为extract_fit_parsnip

library(vip)
#> 
#> Attaching package: 'vip'
#> The following object is masked from 'package:utils':
#> 
#>     vi
library(MASS)
library(tidymodels)

data(Boston, package = "MASS")
df <- Boston

# Split the data into train and test set
set.seed(7)
splits <- initial_split(df)
train <- training(splits)
test <- testing(splits)


# Preprocess with recipe
rec <- recipe(
  formula = medv ~ .,
  data = train
) 

svm_spec <- svm_rbf(margin = 0.0937, cost = 20, rbf_sigma = 0.0208) %>%
  set_engine("kernlab") %>%
  set_mode("regression")


# Putting into workflow
svr_fit <- workflow() %>%
  add_recipe(rec) %>%
  add_model(svm_spec) %>%
  fit(data = train)

svr_fit %>%
  extract_fit_parsnip() %>%
  vip(
    method = "permute", nsim = 5,
    target = "medv", metric = "rmse",
    geom = "point",
    pred_wrapper = function(object, newdata) as.vector(kernlab::predict(object, newdata)),
    train = train
  )

^{创建于2023-12-30，共reprex v2.0.2个}