Tidymodel中的C5_rules()

Question

我想使用tidymodels来拟合一个基于C5.0规则的分类模型。我已将模型指定如下

c5_spec <- 
  C5_rules() %>% 
  set_engine("C5.0") %>% 
  set_mode("classification")

在C5_rules()命令的文档中，我阅读了以下内容。

在对数据使用fit.model_spec()函数之前，不会对模型进行训练或拟合。

在此之后，我不太确定我需要如何处理这个欧洲狙击模型对象。每次我尝试拟合模型时，我都会得到以下错误

预处理器1/1，模型1/1 (预测)：预测错误。C5.0(object= object$fit，newdata = new_data，type = "class")：必须提供树或规则

我遗漏了什么？

非常感谢!

Answer 1

我尝试了Mark的reprex，得到了最后一个命令(conf_mat)的错误。

负载潮流模型及规则

library(tidymodels)
library(rules)
#> 
#> Attaching package: 'rules'
#> The following object is masked from 'package:dials':
#> 
#>     max_rules

# example training dataset
cars_train <- as_tibble(mtcars)

# change the number of cylinders to character for predicting as a class
cars_train <- 
  cars_train %>%
  mutate(cyl = as.character(cyl))

# training df
cars_train
#> # A tibble: 32 × 11
#>      mpg cyl    disp    hp  drat    wt  qsec    vs    am  gear  carb
#>    <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1  21   6      160    110  3.9   2.62  16.5     0     1     4     4
#>  2  21   6      160    110  3.9   2.88  17.0     0     1     4     4
#>  3  22.8 4      108     93  3.85  2.32  18.6     1     1     4     1
#>  4  21.4 6      258    110  3.08  3.22  19.4     1     0     3     1
#>  5  18.7 8      360    175  3.15  3.44  17.0     0     0     3     2
#>  6  18.1 6      225    105  2.76  3.46  20.2     1     0     3     1
#>  7  14.3 8      360    245  3.21  3.57  15.8     0     0     3     4
#>  8  24.4 4      147.    62  3.69  3.19  20       1     0     4     2
#>  9  22.8 4      141.    95  3.92  3.15  22.9     1     0     4     2
#> 10  19.2 6      168.   123  3.92  3.44  18.3     1     0     4     4
#> # … with 22 more rows

# setup recipe with no preprocessing
cars_rec <-
  recipe(cyl ~ ., data = cars_train)

# specify c5 model; no need to set mode (can only be used for classification)
cars_spec <- 
  C5_rules() %>%
  set_engine("C5.0")

# create workflow
cars_wf <-
  workflow() %>%
  add_recipe(cars_rec) %>%
  add_model(cars_spec)

# fit workflow
cars_fit <- fit(cars_wf, data = cars_train)

# add predictions to df
cars_preds <- 
  predict(cars_fit, new_data = cars_train) %>%
  bind_cols(cars_train) %>%
  select(.pred_class, cyl)

cars_preds
#> # A tibble: 32 × 2
#>    .pred_class cyl  
#>    <fct>       <chr>
#>  1 6           6    
#>  2 6           6    
#>  3 4           4    
#>  4 6           6    
#>  5 8           8    
#>  6 6           6    
#>  7 8           8    
#>  8 4           4    
#>  9 4           4    
#> 10 6           6    
#> # … with 22 more rows


# confusion matrix
cars_preds %>%
  conf_mat(truth = cyl, 
           estimate = .pred_class)
#> Error in `yardstick_table()`:
#> ! `truth` must be a factor.
#> ℹ This is an internal error in the yardstick package, please report it to the package authors.

#> Backtrace:
#>     ▆
#>  1. ├─cars_preds %>% conf_mat(truth = cyl, estimate = .pred_class)
#>  2. ├─yardstick::conf_mat(., truth = cyl, estimate = .pred_class)
#>  3. └─yardstick:::conf_mat.data.frame(., truth = cyl, estimate = .pred_class)
#>  4.   └─yardstick:::yardstick_table(truth = truth, estimate = estimate, case_weights = case_weights)
#>  5.     └─rlang::abort("`truth` must be a factor.", .internal = TRUE)