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 function.

But I also want to calculate precision, recall and F1 score.

My question is - do I have to write functions for those myself, or is there any function in R or any of CRAN libraries for that?

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

Answer 2

The ROCR library calculates all these and more (see also http://rocr.bioinf.mpi-sb.mpg.de):

library (ROCR); ...  y <- ... # logical array of positive / negative cases predictions <- ... # array of predictions  pred <- prediction(predictions, y);  # Recall-Precision curve              RP.perf <- performance(pred, "prec", "rec");  plot (RP.perf);  # ROC curve ROC.perf <- performance(pred, "tpr", "fpr"); plot (ROC.perf);  # ROC area under the curve auc.tmp <- performance(pred,"auc"); auc <- as.numeric([email protected])  ...