Confusion Matrix for Multi-class Variables using cross-validation

Question

my dataset includes multi-class variables (11 different variables). All of the columns are numeric except the last column which is the label ( Last Column Name = Movement, 11 different types, Type = text). I want to try different classification algorithms on my datasets using the cross-validation method, and I also want to present different measuring performance such as accuracy, f1, g-measure, recall, and etc. So far, I have used the Caret package but it just shows the Accuracy and Kappa as the result of each algorithm. I wanted to know how can I present other performance measurements while I am using a cross-validation method and my database includes 11 different class variables. I know that the confusion matrix function in R contains most of the performance function that I need but I do not work on my database and it just shows the accuracy. Besides, I need to mention that I have used the Caret package because I need various classification algorithms and the Caret package does include most of them.

library(caret)

library(rpart)

train <- createFolds(Database$Movement, k = 10)

DecisionTree <- Database%>% train(Movement ~ ., method = "rpart", data = ., tuneLength = 5, trControl = trainControl(method = "cv"))

confusionMatrix(DecisionTree)

Answer 1

You should be able to change the objective function via argument to train.

Second, if you are looking for training performance, look at the function ‘resamples’