Easy way of counting precision, recall and F1-score in R

Question

I am using an rpart classifier in R. The question is - I would want to test the trained classifier on a test data. This is fine - I can use the predict.rpart<

Answer 1

using the caret package:

library(caret)

y <- ... # factor of positive / negative cases
predictions <- ... # factor of predictions

precision <- posPredValue(predictions, y, positive="1")
recall <- sensitivity(predictions, y, positive="1")

F1 <- (2 * precision * recall) / (precision + recall)

A generic function that works for binary and multi-class classification without using any package is:

f1_score <- function(predicted, expected, positive.class="1") {
    predicted <- factor(as.character(predicted), levels=unique(as.character(expected)))
    expected  <- as.factor(expected)
    cm = as.matrix(table(expected, predicted))

    precision <- diag(cm) / colSums(cm)
    recall <- diag(cm) / rowSums(cm)
    f1 <-  ifelse(precision + recall == 0, 0, 2 * precision * recall / (precision + recall))

    #Assuming that F1 is zero when it's not possible compute it
    f1[is.na(f1)] <- 0

    #Binary F1 or Multi-class macro-averaged F1
    ifelse(nlevels(expected) == 2, f1[positive.class], mean(f1))
}

Some comments about the function:

• It's assumed that an F1 = NA is zero
• positive.class is used only in binary f1
• for multi-class problems, the macro-averaged F1 is computed
• If predicted and expected had different levels, predicted will receive the expected levels