Confusion Matrix for Multi-class variables using cross validation.

Hi, 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.


train <- createFolds(Database$Movement, k = 10)
DecisionTree <- Database%>% train(Movement ~ .,
  method = "rpart",
  data = .,
  tuneLength = 5,
    trControl = trainControl(method = "cv"))

See Section 5.5.5 of the documentation.

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