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?
