Not able to evaluate models with tidymodels

[boringstuff]

Hi,

I'm following a classification case example (Chapter 7) from Supervised Machine Learning by EMIL HVITFELDT AND JULIA SILGE. The dataset can be downloaded from here finance complaints.

So my question is I installed the required packages and trying to reproduce the result, everything works fine until fit the training data back to the workflow will return "Warning message: naive_bayes(): y has less than two classes. "

library(discrim)
nb_spec <- naive_Bayes() %>%
  set_mode("classification") %>%
  set_engine("naivebayes")
nb_spec

nb_fit <- complaint_wf %>%
  add_model(nb_spec) %>%
  fit(data = complaints_train)

set.seed(234)
complaints_folds <- vfold_cv(complaints_train)

complaints_folds

nb_wf <- workflow() %>%
  add_recipe(complaints_rec) %>%
  add_model(nb_spec)

nb_wf

nb_rs <- fit_resamples(
  nb_wf,
  complaints_folds,
  control = control_resamples(save_pred = TRUE)
)

After folding process, the model is not able to evaluate and return
"! Fold01: preprocessor 1/1, model 1/1: naive_bayes(): y has less than two classes.
x Fold01: internal: Error: In metric: accuracy
Problem with summarise() input .estimate.
x `estima..."

I have no idea where went wrong...

Thanks.

My R sessionInfo.

R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] stopwords_2.1     naivebayes_0.9.7  vctrs_0.3.6       rlang_0.4.10      discrim_0.1.1     textrecipes_0.4.0
 [7] yardstick_0.0.7   workflows_0.2.1   tune_0.1.2        rsample_0.0.8     recipes_0.1.15    parsnip_0.1.4    
[13] modeldata_0.1.0   infer_0.5.3       dials_0.0.9       scales_1.1.1      broom_0.7.3       tidymodels_0.1.2 
[19] forcats_0.5.0     stringr_1.4.0     dplyr_1.0.2       purrr_0.3.4       readr_1.4.0       tidyr_1.1.2      
[25] tibble_3.0.4      ggplot2_3.3.3     tidyverse_1.3.0  

loaded via a namespace (and not attached):
 [1] fs_1.5.0           usethis_2.0.0      lubridate_1.7.9.2  DiceDesign_1.8-1   httr_1.4.2        
 [6] SnowballC_0.7.0    tools_4.0.3        backports_1.2.1    utf8_1.1.4         R6_2.5.0          
[11] rpart_4.1-15       DBI_1.1.0          colorspace_2.0-0   nnet_7.3-14        withr_2.3.0       
[16] tidyselect_1.1.0   compiler_4.0.3     cli_2.2.0          rvest_0.3.6        xml2_1.3.2        
[21] digest_0.6.27      pkgconfig_2.0.3    parallelly_1.23.0  lhs_1.1.1          dbplyr_2.0.0      
[26] readxl_1.3.1       rstudioapi_0.13    generics_0.1.0     jsonlite_1.7.2     tokenizers_0.2.1  
[31] magrittr_2.0.1     Matrix_1.3-0       Rcpp_1.0.5         munsell_0.5.0      fansi_0.4.1       
[36] GPfit_1.0-8        lifecycle_0.2.0    furrr_0.2.1        stringi_1.5.3      pROC_1.16.2       
[41] MASS_7.3-53        plyr_1.8.6         grid_4.0.3         parallel_4.0.3     listenv_0.8.0     
[46] crayon_1.3.4       lattice_0.20-41    haven_2.3.1        splines_4.0.3      hms_0.5.3         
[51] pillar_1.4.7       codetools_0.2-18   reprex_0.3.0       glue_1.4.2         modelr_0.1.8      
[56] foreach_1.5.1      cellranger_1.1.0   gtable_0.3.0       future_1.21.0      assertthat_0.2.1  
[61] gower_0.2.2        prodlim_2019.11.13 class_7.3-17       survival_3.2-7     timeDate_3043.102 
[66] iterators_1.0.13   hardhat_0.1.5      lava_1.6.8.1       globals_0.14.0     ellipsis_0.3.1    
[71] ipred_0.9-9       

[Max]

@boringstuff

"! Fold01: preprocessor 1/1, model 1/1: naive_bayes(): y has less than two classes.

This model requires that all the levels of the outcome be available in each unique level of the categorical predictors. For example, if predictor "A" only has a single class, the model will fail.

Can you screen the data to see if that's the case? I would but there isn't a reprepx since I can't see the code that made complaints_train.

[boringstuff]

I think I kind of got it now, indeed after cleaning the data there is only one class. I will try with a different dataset, will get back to you. Thanks!