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

警告讯息：在eval（xpr，envir = envir）中：模型适合Fold01.Rep1失败：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：

请帮助我理解此消息以及我需要采取哪些步骤来避免模型拟合失败。我觉得我需要从DTM中删除更多稀疏术语，但我不确定。

构建模型的代码：

    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'))

构建文档术语矩阵的代码（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)

Answer 1

这意味着，当重新采样这些变量时，它们只有一个唯一值。您可以使用preProc = "zv"来消除警告。对于这些问题，这将有助于获得一个小的，可重现的例子。

带插入符号的Text2Vec分类 - 朴素贝叶斯警告消息

1 个答案: