我用朴素贝叶斯进行了分类。目标是通过文本预测4个因素。数据如下所示：
'data.frame': 387 obs. of 2 variables:
$ reviewText: chr "I love this. I have a D800. I am mention my camera to make sure that you understand that this product is not ju"| __truncated__ "I hate buying larger gig memory cards - because there's always that greater risk of losing the photos, and/or r"| __truncated__ "These chromebooks are really a pretty nice idea -- Almost no maintaince (no maintaince?), no moving parts, smal"| __truncated__ "Purchased, as this drive allows a much speedier read/write and is just below a full SSD (they need to drop the "| __truncated__ ...
$ pragmatic : Factor w/ 4 levels "-1","0","1","9": 4 4 4 3 3 4 3 3 3...
我使用caret包进行了分类。分类代码如下：
sms_corpus <- Corpus(VectorSource(sms_raw$text))
sms_corpus_clean <- sms_corpus %>%
tm_map(content_transformer(tolower)) %>%
tm_map(removeNumbers) %>%
tm_map(removeWords, stopwords(kind="en")) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)
sms_dtm <- DocumentTermMatrix(sms_corpus_clean)
train_index <- createDataPartition(sms_raw$type, p=0.5, list=FALSE)
sms_raw_train <- sms_raw[train_index,]
sms_raw_test <- sms_raw[-train_index,]
sms_corpus_clean_train <- sms_corpus_clean[train_index]
sms_corpus_clean_test <- sms_corpus_clean[-train_index]
sms_dtm_train <- sms_dtm[train_index,]
sms_dtm_test <- sms_dtm[-train_index,]
sms_dict <- findFreqTerms(sms_dtm_train, lowfreq= 5)
sms_train <- DocumentTermMatrix(sms_corpus_clean_train, list(dictionary=sms_dict))
sms_test <- DocumentTermMatrix(sms_corpus_clean_test, list(dictionary=sms_dict))
convert_counts <- function(x) {
x <- ifelse(x > 0, 1, 0)
x <- factor(x, levels = c(0, 1), labels = c("Absent", "Present"))
}
sms_train <- sms_train %>% apply(MARGIN=2, FUN=convert_counts)
sms_test <- sms_test %>% apply(MARGIN=2, FUN=convert_counts)
ctrl <- trainControl(method="cv", 10)
set.seed(8)
sms_model1 <- train(sms_train, sms_raw_train$type, method="nb",
trControl=ctrl)
sms_predict1 <- predict(sms_model1, sms_test)
cm1 <- confusionMatrix(sms_predict1, sms_raw_test$type)
当我以这种方式使用这个模型时，这意味着我同时对所有4个变量进行预测，得到一个低Accuracy:0.5469，混淆矩阵看起来像这样。
Reference
Prediction -1 0 1 9
-1 0 0 1 0
0 0 0 0 0
1 9 5 33 25
9 11 3 33 72
当我分别对所有4个变量进行预测时，我得到了更好的结果。分类的代码与上述相同，但我代替df$sensorial <- factor(df$sensorial) df$sensorial <- as.factor(df$sensorial == 9)。对于其他变量，我使用1，-1或0代替9。如果我这样做，我会Accuracy: 0.772获得9，Accuracy:0.829获得-1，Accuracy:0.9016获得0， Accuracy:0.7959的{{1}}。另外，结果要好得多。所以它必须与特征选择有关。结果不同的原因可能是不同值的特征通常是相同的。因此，一种可能的解决方案可能是使这些特征更加重要，这些特征仅在存在某个值但不存在其他特征时才会发生。有没有办法以这种方式选择特征，这样如果我同时对所有4个变量进行预测，模型会更好？像加权的术语 - 文档矩阵？
修改
我计算了Cihan Ceyhan所说的四个值的权重：
1但结果并不是更好prop.table(table(sms_raw_train$type))
-1 0 1 9
0.025773196 0.005154639 0.180412371 0.788659794
modelweights <- ifelse(sms_raw_train$type == -1,
(1/table(sms_raw_train$type)[1]) * 0.25,
ifelse(sms_raw_train$type == 0,
(1/table(sms_raw_train$type)[2]) * 0.25,
ifelse(sms_raw_train$type == 1,
(1/table(sms_raw_train$type)[3]) * 0.25,
ifelse(sms_raw_train$type == 9,
(1/table(sms_raw_train$type)[4]) * 0.25,9))))
Accuracy:0.5677因此，分别计算每个值的结果可能是一个更好的主意，然后将结果加总，就像发布的第二个解决方案一样。