Confusion Matrix

The confusion matrix allows visualization of the performance of a classification algorithm.

In this blog post, a function is shown to create an image of the confusion matrix.

The R package caret includes the confusionMatrix function, which provides a comprehensive output.

Code

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

Classification

We will perform a Naive Bayes classification on the classical iris data set.

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          9         0
  virginica       0          1        10

Overall Statistics
                                          
               Accuracy : 0.9667          
                 95% CI : (0.8278, 0.9992)
    No Information Rate : 0.3333          
    P-Value [Acc > NIR] : 2.963e-13       
                                          
                  Kappa : 0.95            
                                          
 Mcnemar's Test P-Value : NA              

Statistics by Class:

                     Class: setosa Class: versicolor Class: virginica
Sensitivity                 1.0000            0.9000           1.0000
Specificity                 1.0000            1.0000           0.9500
Pos Pred Value              1.0000            1.0000           0.9091
Neg Pred Value              1.0000            0.9524           1.0000
Prevalence                  0.3333            0.3333           0.3333
Detection Rate              0.3333            0.3000           0.3333
Detection Prevalence        0.3333            0.3000           0.3667
Balanced Accuracy           1.0000            0.9500           0.9750

Plotting

To plot the obtained confusion matrix as a ggplot graphic, we will 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)

Confusion Matrix in R

Confusion Matrix

Classification

Plotting

Links