Need Help on Text Mining Confusion Matrix

[franklee20]

training <- sample(c(1:2000), 0.6*2000)

trainData = cbind(label = label[training], words.df[training,])
reg <- glm(label ~ ., data = trainData, family = 'binomial')

validData = cbind(label = label[-training], words.df[-training,])
pred <- predict(reg, newdata = validData, type = "response")

library(caret)
confusionMatrix(ifelse(pred>0.5, 1, 0), label[-training])

However, it doesn't produce confusion matrix.
Therefore, I tried with

confusionMatrix(as.factor(ifelse(pred>0.5), as.factor(label[-training])

But, nothing is working. I will appreciate if you can point out the issue and/or suggestion.

[siddharthprabhu]

Hello @franklee20, welcome to RStudio Community.

It would help a lot if you could provide a reproducible example by following the guide below. We don't have the label or words.df objects so it's hard to see what's happening.

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…