Return to Answer

deleted 4 characters in body

Source Link

edited Dec 9, 2017 at 14:01

2.7k
1
12
25

It is almost the same as what you already have. You just need to specify the test data in your predict statement.

testtable <- table(trainingset$Regiontestset$Region, 
    predict(tree.1, newdata=testset, type="class"))

#Accuracy for each fold
Accuracy[i] = sum(diag(testtable))/length(testset[,1])

It is almost the same as what you already have. You just need to specify the test data in your predict statement.

testtable <- table(trainingset$Region, 
    predict(tree.1, newdata=testset, type="class"))

#Accuracy for each fold
Accuracy[i] = sum(diag(testtable))/length(testset[,1])

It is almost the same as what you already have. You just need to specify the test data in your predict statement.

testtable <- table(testset$Region, 
    predict(tree.1, newdata=testset, type="class"))

#Accuracy for each fold
Accuracy[i] = sum(diag(testtable))/length(testset[,1])

Source Link

created Dec 9, 2017 at 1:22

G5W

2.7k
1
12
25

It is almost the same as what you already have. You just need to specify the test data in your predict statement.

testtable <- table(trainingset$Region, 
    predict(tree.1, newdata=testset, type="class"))

#Accuracy for each fold
Accuracy[i] = sum(diag(testtable))/length(testset[,1])