How to compute logarithmic loss with the probability output of binary classification

Question

The following code are used to produce the probability output of binary classification with Random Forest.

library(randomForest) 

rf <- randomForest(train, train_label,importance=TRUE,proximity=TRUE)
prediction<-predict(rf, test, type="prob")

Then the result about prediction is as follows:

The true label about test data are known (named test_label). Now I want to compute logarithmic loss for probability output of binary classification. The function about LogLoss is as follows.

LogLoss=function(actual, predicted)
{
  result=-1/length(actual)*(sum((actual*log(predicted)+(1-actual)*log(1-predicted))))
  return(result)
}

How to compute logarithmic loss with probability output of binary classification. Thank you.

Answer 1

Looks like you already have everything you need, assuming you also have a vector of labels for the test set (call it test_labels). You can now just say LogLoss(test_labels, prediction[,1]).