How do I calculate accuracy, precision and recall for each class from a Naive Bayes model?
I am using the embedded dataset: iris and package tree and package e1071 for Naive Bayes. The confusion matrix is as below:
prediction setosa versicolor virginica
setosa 29 0 0
versicolor 0 20 2
virginica 0 3 21
P.S: note that i am using 75 entries as the training set and other for testing:
iris.train <- c(sample(1:150, 75)) # have selected 75 randomly
Throughout this answer, mat is the confusion matrix that you describe.
You can calculate and store accuracy with:
(accuracy <- sum(diag(mat)) / sum(mat))
# [1] 0.9333333
Precision for each class (assuming the predictions are on the rows and the true outcomes are on the columns) can be computed with:
(precision <- diag(mat) / rowSums(mat))
# setosa versicolor virginica
# 1.0000000 0.9090909 0.8750000
If you wanted to grab the precision for a particular class, you could do:
(precision.versicolor <- precision["versicolor"])
# versicolor
# 0.9090909
Recall for each class (again assuming the predictions are on the rows and the true outcomes are on the columns) can be calculated with:
recall <- (diag(mat) / colSums(mat))
# setosa versicolor virginica
# 1.0000000 0.8695652 0.9130435
If you wanted recall for a particular class, you could do something like:
(recall.virginica <- recall["virginica"])
# virginica
# 0.9130435
If instead you had the true outcomes as the rows and the predicted outcomes as the columns, then you would flip the precision and recall definitions.
Data:
(mat = as.matrix(read.table(text=" setosa versicolor virginica
setosa 29 0 0
versicolor 0 20 2
virginica 0 3 21", header=T)))
# setosa versicolor virginica
# setosa 29 0 0
# versicolor 0 20 2
# virginica 0 3 21
