带插入符号的Text2Vec分类 - 朴素贝叶斯警告消息

有关更多上下文，请参阅question listed here .

我尝试使用 text2vec 构建的文档术语矩阵，使用 caret 包训练一个朴素的贝叶斯（ nb ）模型 . 但是，我收到此警告消息：

警告消息：在eval（xpr，envir = envir）中：Fold01的模型拟合失败.Rep1：usekernel = FALSE，fL = 0，adjust = 1 NaiveBayes.default中的错误（x，y，usekernel = FALSE，fL = param $ fL，...）：变量中至少有一个类的零差异：

请帮助我理解这个消息以及我需要采取哪些步骤来避免模型拟合失败 . 我有一种感觉，我需要从DTM中删除更多稀疏术语，但我不确定 .

Code to build the model:

control <- trainControl(method="repeatedcv", number=10, repeats=3, savePredictions=TRUE, classProbs=TRUE)

    Train_PRDHA_String.df$Result <- ifelse(Train_PRDHA_String.df$Result == 1, "X", "Y")

    (warn=1)
    (warnings=2)

  t4 = Sys.time()
  svm_nb <- train(x = as.matrix(dtm_train), y = as.factor(Train_PRDHA_String.df$Result),
                  method = "nb",
                  trControl=control,
                  tuneLength = 5,
                  metric ="Accuracy")
print(difftime(Sys.time(), t4, units = 'sec'))

Code to build the Document Term Matrix (Text2Vec):

library(text2vec)
library(data.table)

#Define preprocessing function and tokenization fucntion
preproc_func = tolower
token_func = word_tokenizer

#Union both of the Text fields - learn vocab from both fields
union_txt = c(Train_PRDHA_String.df$MAKTX_Keyword, Train_PRDHA_String.df$PH_Level_04_Description_Keyword)

#Create an iterator over tokens with the itoken() function
it_train = itoken(union_txt, 
                  preprocessor = preproc_func, 
                  tokenizer = token_func, 
                  ids = Train_PRDHA_String.df$ID, 
                  progressbar = TRUE)

#Build Vocabulary
vocab = create_vocabulary(it_train)

vocab

#Dimensional Reduction
pruned_vocab = prune_vocabulary(vocab, 
                                term_count_min = 10, 
                                doc_proportion_max = 0.5,
                                doc_proportion_min = 0.001)
vectorizer = vocab_vectorizer(pruned_vocab)

#Start building a document-term matrix
#vectorizer = vocab_vectorizer(vocab)

#learn vocabulary from Train_PRDHA_String.df$MAKTX_Keyword
it1 = itoken(Train_PRDHA_String.df$MAKTX_Keyword, preproc_func, 
             token_func, ids = Train_PRDHA_String.df$ID)
dtm_train_1 = create_dtm(it1, vectorizer)

#learn vocabulary from Train_PRDHA_String.df$PH_Level_04_Description_Keyword
it2 = itoken(Train_PRDHA_String.df$PH_Level_04_Description_Keyword, preproc_func, 
             token_func, ids = Train_PRDHA_String.df$ID)
dtm_train_2 = create_dtm(it2, vectorizer)

#Combine dtm1 & dtm2 into a single matrix
dtm_train = cbind(dtm_train_1, dtm_train_2)

#Normalise
dtm_train = normalize(dtm_train, "l1")

dim(dtm_train)