1 Confusion Matrix

The confusion matrix is a useful tool for visualizing the performance of a classification algorithm. In this blog post, we provide a function to generate an image of the confusion matrix. Additionally, the R package caret includes the confusionMatrix function, which produces detailed output.

Code

library(e1071)
library(caTools)
library(ggplot2)
library(scales)
library(caret)

1.1 Classification

We will conduct a Naive Bayes classification using the classic Iris dataset.

Code

# train and test data
iris$spl <- caTools::sample.split(iris, SplitRatio = 0.8)
train <- subset(iris, iris$spl == TRUE)
test <- subset(iris, iris$spl == FALSE)

iris_nb <- naiveBayes(Species ~ ., data = train)
nb_train_predict <- predict(iris_nb, test[, names(test) != "Species"])

cfm <- confusionMatrix(nb_train_predict, test$Species)
cfm

Confusion Matrix and Statistics

            Reference
Prediction   setosa versicolor virginica
  setosa         10          0         0
  versicolor      0         10         2
  virginica       0          0         8

Overall Statistics
                                          
               Accuracy : 0.9333          
                 95% CI : (0.7793, 0.9918)
    No Information Rate : 0.3333          
    P-Value [Acc > NIR] : 8.747e-12       
                                          
                  Kappa : 0.9             
                                          
 Mcnemar's Test P-Value : NA              

Statistics by Class:

                     Class: setosa Class: versicolor Class: virginica
Sensitivity                 1.0000            1.0000           0.8000
Specificity                 1.0000            0.9000           1.0000
Pos Pred Value              1.0000            0.8333           1.0000
Neg Pred Value              1.0000            1.0000           0.9091
Prevalence                  0.3333            0.3333           0.3333
Detection Rate              0.3333            0.3333           0.2667
Detection Prevalence        0.3333            0.4000           0.2667
Balanced Accuracy           1.0000            0.9500           0.9000

1.2 Plotting

To plot the resulting confusion matrix using ggplot, we use the following function:

Code

ggplot_confusion_matrix <- function(cfm) {
  mytitle <- paste("Accuracy", percent_format() (cfm$overall[1]),
                   "Kappa", percent_format() (cfm$overall[2]))
  p <-
    ggplot(data = as.data.frame(cfm$table),
           aes(x = Reference, y = Prediction)) +
    geom_tile(aes(fill = log(Freq)), colour = "white") +
    scale_fill_gradient(low = "white", high = "steelblue") +
    geom_text(aes(x = Reference, y = Prediction, label = Freq)) +
    theme(legend.position = "none") +
    ggtitle(mytitle)
  return(p)
}

Code

ggplot_confusion_matrix(cfm)

1.3 References

Burns C, Herrero EP (2017) How to produce a confusion matrix and find the misclassification rate of the naïve bayes classifier?, 2017. Available from: https://stackoverflow.com/questions/46063234/how-to-produce-a-confusion-matrix-and-find-the-misclassification-rate-of-the-na%c3%af/46063613#46063613.