R how to visualize confusion matrix using the caret package

Question

I'd like to visualize the data I've put in the confusion matrix. Is there a function I could simply put the confusion matrix and it would visualize it (plot it)?

Example what I'd like to do(Matrix$nnet is simply a table containing results from the classification):

Confusion$nnet <- confusionMatrix(Matrix$nnet) plot(Confusion$nnet) 

My Confusion$nnet$table looks like this:

    prediction (I would also like to get rid of this string, any help?)     1  2 1   42 6 2   8 28 

Answer 1

You can just use the rect functionality in r to layout the confusion matrix. Here we will create a function that allows the user to pass in the cm object created by the caret package in order to produce the visual.

Let's start by creating an evaluation dataset as done in the caret demo:

# construct the evaluation dataset set.seed(144) true_class <- factor(sample(paste0("Class", 1:2), size = 1000, prob = c(.2, .8), replace = TRUE)) true_class <- sort(true_class) class1_probs <- rbeta(sum(true_class == "Class1"), 4, 1) class2_probs <- rbeta(sum(true_class == "Class2"), 1, 2.5) test_set <- data.frame(obs = true_class,Class1 = c(class1_probs, class2_probs)) test_set$Class2 <- 1 - test_set$Class1 test_set$pred <- factor(ifelse(test_set$Class1 >= .5, "Class1", "Class2")) 

Now let's use caret to calculate the confusion matrix:

# calculate the confusion matrix cm <- confusionMatrix(data = test_set$pred, reference = test_set$obs) 

Now we create a function that lays out the rectangles as needed to showcase the confusion matrix in a more visually appealing fashion:

draw_confusion_matrix <- function(cm) {    layout(matrix(c(1,1,2)))   par(mar=c(2,2,2,2))   plot(c(100, 345), c(300, 450), type = "n", xlab="", ylab="", xaxt='n', yaxt='n')   title('CONFUSION MATRIX', cex.main=2)    # create the matrix    rect(150, 430, 240, 370, col='#3F97D0')   text(195, 435, 'Class1', cex=1.2)   rect(250, 430, 340, 370, col='#F7AD50')   text(295, 435, 'Class2', cex=1.2)   text(125, 370, 'Predicted', cex=1.3, srt=90, font=2)   text(245, 450, 'Actual', cex=1.3, font=2)   rect(150, 305, 240, 365, col='#F7AD50')   rect(250, 305, 340, 365, col='#3F97D0')   text(140, 400, 'Class1', cex=1.2, srt=90)   text(140, 335, 'Class2', cex=1.2, srt=90)    # add in the cm results    res <- as.numeric(cm$table)   text(195, 400, res[1], cex=1.6, font=2, col='white')   text(195, 335, res[2], cex=1.6, font=2, col='white')   text(295, 400, res[3], cex=1.6, font=2, col='white')   text(295, 335, res[4], cex=1.6, font=2, col='white')    # add in the specifics    plot(c(100, 0), c(100, 0), type = "n", xlab="", ylab="", main = "DETAILS", xaxt='n', yaxt='n')   text(10, 85, names(cm$byClass[1]), cex=1.2, font=2)   text(10, 70, round(as.numeric(cm$byClass[1]), 3), cex=1.2)   text(30, 85, names(cm$byClass[2]), cex=1.2, font=2)   text(30, 70, round(as.numeric(cm$byClass[2]), 3), cex=1.2)   text(50, 85, names(cm$byClass[5]), cex=1.2, font=2)   text(50, 70, round(as.numeric(cm$byClass[5]), 3), cex=1.2)   text(70, 85, names(cm$byClass[6]), cex=1.2, font=2)   text(70, 70, round(as.numeric(cm$byClass[6]), 3), cex=1.2)   text(90, 85, names(cm$byClass[7]), cex=1.2, font=2)   text(90, 70, round(as.numeric(cm$byClass[7]), 3), cex=1.2)    # add in the accuracy information    text(30, 35, names(cm$overall[1]), cex=1.5, font=2)   text(30, 20, round(as.numeric(cm$overall[1]), 3), cex=1.4)   text(70, 35, names(cm$overall[2]), cex=1.5, font=2)   text(70, 20, round(as.numeric(cm$overall[2]), 3), cex=1.4) }   

Finally, pass in the cm object that we calculated when using caret to create the confusion matrix:

draw_confusion_matrix(cm) 

And here are the results: