我试图为mushroom data构建一个简单的Naive Bayes classifer，我想使用所有变量作为分类预测因子来预测蘑菇是否可以食用。
我用的是caret包。
下面是我的代码全文：

##################################################################################
# Prepare R and R Studio environment
##################################################################################

# Clear the R studio console
cat("\014")

# Remove objects from environment
rm(list = ls())

# Install and load packages if necessary
if (!require(tidyverse)) {
  install.packages("tidyverse")
  library(tidyverse)
}
if (!require(caret)) {
  install.packages("caret")
  library(caret)
}
if (!require(klaR)) {
  install.packages("klaR")
  library(klaR)
}

#################################

mushrooms <- read.csv("agaricus-lepiota.data", stringsAsFactors = TRUE, header = FALSE)

na.omit(mushrooms)

names(mushrooms) <- c("edibility", "capShape", "capSurface", "cap-color", "bruises", "odor", "gill-attachment", "gill-spacing", "gill-size", "gill-color", "stalk-shape", "stalk-root", "stalk-surface-above-ring", "stalk-surface-below-ring", "stalk-color-above-ring", "stalk-color-below-ring", "veil-type", "veil-color", "ring-number", "ring-type", "spore-print-color", "population", "habitat")

# convert bruises to a logical variable
mushrooms$bruises <- mushrooms$bruises == 't'

set.seed(1234)
split <- createDataPartition(mushrooms$edibility, p = 0.8, list = FALSE)

train <- mushrooms[split, ]
test <- mushrooms[-split, ]

predictors <- names(train)[2:20] #Create response and predictor data

x <- train[,predictors] #predictors
y <- train$edibility #response

train_control <- trainControl(method = "cv", number = 1) # Set up 1 fold cross validation

edibility_mod1 <- train( #train the model
  x = x,
  y = y,
  method = "nb", 
  trControl = train_control
)

当执行train（）函数时，我得到以下输出：

Something is wrong; all the Accuracy metric values are missing:
    Accuracy       Kappa    
 Min.   : NA   Min.   : NA  
 1st Qu.: NA   1st Qu.: NA  
 Median : NA   Median : NA  
 Mean   :NaN   Mean   :NaN  
 3rd Qu.: NA   3rd Qu.: NA  
 Max.   : NA   Max.   : NA  
 NA's   :2     NA's   :2    
Error: Stopping
In addition: Warning messages:
1: predictions failed for Fold1: usekernel= TRUE, fL=0, adjust=1 Error in predict.NaiveBayes(modelFit, newdata) : 
  Not all variable names used in object found in newdata
 
2: model fit failed for Fold1: usekernel=FALSE, fL=0, adjust=1 Error in x[, 2] : subscript out of bounds
 
3: In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,  :
  There were missing values in resampled performance measures.

脚本运行后的x和y：

> str(x)
'data.frame':   6500 obs. of  19 variables:
 $ capShape                : Factor w/ 6 levels "b","c","f","k",..: 6 6 1 6 6 6 1 1 6 1 ...
 $ capSurface              : Factor w/ 4 levels "f","g","s","y": 3 3 3 4 3 4 3 4 4 3 ...
 $ cap-color               : Factor w/ 10 levels "b","c","e","g",..: 5 10 9 9 4 10 9 9 9 10 ...
 $ bruises                 : logi  TRUE TRUE TRUE TRUE FALSE TRUE ...
 $ odor                    : Factor w/ 9 levels "a","c","f","l",..: 7 1 4 7 6 1 1 4 7 1 ...
 $ gill-attachment         : Factor w/ 2 levels "a","f": 2 2 2 2 2 2 2 2 2 2 ...
 $ gill-spacing            : Factor w/ 2 levels "c","w": 1 1 1 1 2 1 1 1 1 1 ...
 $ gill-size               : Factor w/ 2 levels "b","n": 2 1 1 2 1 1 1 1 2 1 ...
 $ gill-color              : Factor w/ 12 levels "b","e","g","h",..: 5 5 6 6 5 6 3 6 8 3 ...
 $ stalk-shape             : Factor w/ 2 levels "e","t": 1 1 1 1 2 1 1 1 1 1 ...
 $ stalk-root              : Factor w/ 5 levels "?","b","c","e",..: 4 3 3 4 4 3 3 3 4 3 ...
 $ stalk-surface-above-ring: Factor w/ 4 levels "f","k","s","y": 3 3 3 3 3 3 3 3 3 3 ...
 $ stalk-surface-below-ring: Factor w/ 4 levels "f","k","s","y": 3 3 3 3 3 3 3 3 3 3 ...
 $ stalk-color-above-ring  : Factor w/ 9 levels "b","c","e","g",..: 8 8 8 8 8 8 8 8 8 8 ...
 $ stalk-color-below-ring  : Factor w/ 9 levels "b","c","e","g",..: 8 8 8 8 8 8 8 8 8 8 ...
 $ veil-type               : Factor w/ 1 level "p": 1 1 1 1 1 1 1 1 1 1 ...
 $ veil-color              : Factor w/ 4 levels "n","o","w","y": 3 3 3 3 3 3 3 3 3 3 ...
 $ ring-number             : Factor w/ 3 levels "n","o","t": 2 2 2 2 2 2 2 2 2 2 ...
 $ ring-type               : Factor w/ 5 levels "e","f","l","n",..: 5 5 5 5 1 5 5 5 5 5 ...


> str(y)
 Factor w/ 2 levels "e","p": 2 1 1 2 1 1 1 1 2 1 ...

我的环境是：

> R.version
               _                           
platform       x86_64-apple-darwin17.0     
arch           x86_64                      
os             darwin17.0                  
system         x86_64, darwin17.0          
status                                     
major          4                           
minor          0.3                         
year           2020                        
month          10                          
day            10                          
svn rev        79318                       
language       R                           
version.string R version 4.0.3 (2020-10-10)
nickname       Bunny-Wunnies Freak Out     
> RStudio.Version()
$citation

To cite RStudio in publications use:

  RStudio Team (2020). RStudio: Integrated Development Environment for R. RStudio, PBC, Boston, MA URL http://www.rstudio.com/.

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {RStudio: Integrated Development Environment for R},
    author = {{RStudio Team}},
    organization = {RStudio, PBC},
    address = {Boston, MA},
    year = {2020},
    url = {http://www.rstudio.com/},
  }

$mode
[1] "desktop"

$version
[1] ‘1.3.1093’

$release_name
[1] "Apricot Nasturtium"