R中的朴素贝叶斯错误：下标超出范围

Question

I'm trying to classify 94 text of speech. Since naiveBayes cannot work well if categories of trainset do not exist in categories of testset, I randomized and confirmed. There were no problem with categories. But classifier didn't work with testset. Following is error message:

Df.dtm<-cbind(Df.dtm, category)
dim(Df.dtm)
Df.dtm[1:10, 530:532]

# Randomize and Split data by rownumber
train <- sample(nrow(Df.dtm), ceiling(nrow(Df.dtm) * .50))
test <- (1:nrow(Df.dtm))[- train]

# Isolate classifier
cl <- Df.dtm[, "category"]
> summary(cl[train])
  dip  eds  ind pols 
  23    8    3   13 

# Create model data and remove "category"
modeldata <- Df.dtm[,!colnames(Df.dtm) %in% "category"]

#Boolean feature Multinomial Naive Bayes
#Function to convert the word frequencies to yes and no labels
convert_count <- function(x) {
  y <- ifelse(x > 0, 1,0)
  y <- factor(y, levels=c(0,1), labels=c("No", "Yes"))
  y
}

#Apply the convert_count function to get final training and testing DTMs
train.cc <- apply(modeldata[train, ], 2, convert_count)
test.cc <- apply(modeldata[test, ], 2, convert_count)

#Training the Naive Bayes Model
#Train the classifier
system.time(classifier <- naiveBayes(train.cc, cl[train], laplace = 1) )

This classifier worked well: 用户 系统 流逝 0.45 0.00 0.46

#Use the classifier we built to make predictions on the test set.
system.time(pred <- predict(classifier, newdata=test.cc))

However, prediction failed. Error in [.default (object$tables[[v]], , nd) : 下标出界 Timing stopped at: 0.2 0 0.2

Answer 1

Consider the following:

# Indicies of training observations as observations.
train <- sample(nrow(Df.dtm), ceiling(nrow(Df.dtm) * .50))

# Indicies of whatever is left over from the previous sample, again, also observations are being returned. 
#that still remains inside of Df.dtm, notation as follows:
test <- Df.dtm[-train,]

After clearing up what my sample returned (row indicies) and how I wanted to slice up my test set (again, rows or columns need to be established at this point), the I would tweak that apply function with the argument necessary here is a link of how the apply function works , but for the sake of time, if you pass it a 2 you apply over each column and if you pass it a 1 it will apply the function given over each row . Again, depending on how you want your sample (rows or columns) we can tweak this either way.