Calculating prediction accuracy of a tree using rp

Question

I have constructed a decision tree using rpart for a dataset.

I have then divided the data into 2 parts - a training dataset and a test dataset. A tree has been constructed for the dataset using the training data. I want to calculate the accuracy of the predictions based on the model that was created.

My code is shown below:

library(rpart)
#reading the data
data = read.table("source")
names(data) <- c("a", "b", "c", "d", "class")

#generating test and train data - Data selected randomly with a 80/20 split
trainIndex  <- sample(1:nrow(x), 0.8 * nrow(x))
train <- data[trainIndex,]
test <- data[-trainIndex,]

#tree construction based on information gain
tree = rpart(class ~ a + b + c + d, data = train, method = 'class', parms = list(split = "information"))

I now want to calculate the accuracy of the predictions generated by the model by comparing the results with the actual values train and test data however I am facing an error while doing so.

My code is shown below:

t_pred = predict(tree,test,type="class")
t = test['class']
accuracy = sum(t_pred == t)/length(t)
print(accuracy)

I get an error message that states -

Error in t_pred == t : comparison of these types is not implemented In addition: Warning message: Incompatible methods ("Ops.factor", "Ops.data.frame") for "=="

On checking the type of t_pred, I found out that it is of type integer however the documentation

(https://stat.ethz.ch/R-manual/R-devel/library/rpart/html/predict.rpart.html)

states that the predict() method must return a vector.

I am unable to understand why is the type of the variable is an integer and not a list. Where have I made the mistake and how can I fix it?

Answer 1

My response is very similar to @mtoto's one but a bit more simply... I hope it also helps.

mean(test$class == t_pred)

Answer 2

Try calculating the confusion matrix first:

confMat <- table(test$class,t_pred)

Now you can calculate the accuracy by dividing the sum diagonal of the matrix - which are the correct predictions - by the total sum of the matrix:

accuracy <- sum(diag(confMat))/sum(confMat)