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

Simplest way, incorporating caret:

library(caret)
library(yardstick)
library(ggplot2)

Train model

plsFit <- train(
  y ~ .,
  data = trainData
)

Get predictions from model

plsClasses <- predict(plsFit, newdata = testdata)

truth_predicted<-data.frame(
  obs = testdata$y,
  pred = plsClasses
)

Make matrix. Notice obs and pred aren't strings

cm <- conf_mat(truth_predicted, obs, pred)

Plot

autoplot(cm, type = "heatmap") +
  scale_fill_gradient(low="#D6EAF8",high = "#2E86C1")

Answer 2

cvms has plot_confusion_matrix() as well with some bells and whistles:

# Create targets and predictions data frame
data <- data.frame(
  "target" = c("A", "B", "A", "B", "A", "B", "A", "B",
               "A", "B", "A", "B", "A", "B", "A", "A"),
  "prediction" = c("B", "B", "A", "A", "A", "B", "B", "B",
                   "B", "B", "A", "B", "A", "A", "A", "A"),
  stringsAsFactors = FALSE
)

# Evaluate predictions and create confusion matrix
eval <- evaluate(
  data = data,
  target_col = "target",
  prediction_cols = "prediction",
  type = "binomial"
)

eval

> # A tibble: 1 x 19
>   `Balanced Accuracy` Accuracy    F1 Sensitivity Specificity `Pos Pred Value` `Neg Pred Value`   AUC `Lower CI`
>                 <dbl>    <dbl> <dbl>       <dbl>       <dbl>            <dbl>            <dbl> <dbl>      <dbl>
> 1               0.690    0.688 0.667       0.714       0.667            0.625             0.75 0.690      0.447
> # … with 10 more variables: Upper CI <dbl>, Kappa <dbl>, MCC <dbl>, Detection Rate <dbl>,
> #   Detection Prevalence <dbl>, Prevalence <dbl>, Predictions <list>, ROC <named list>, Confusion Matrix <list>,
> #   Process <list>

# Plot confusion matrix
# Either supply confusion matrix tibble directly
plot_confusion_matrix(eval[["Confusion Matrix"]][[1]])

# Or plot first confusion matrix in evaluate() output
plot_confusion_matrix(eval)

The output is a ggplot object.

Answer 3

I know this is quite late, but I was looking for a solution my self. Working on some of the previous answers above, in addition to this post. Using ggplot2 package and base table function, I made this simple function to plot a nicely colored confusion matrix:

conf_matrix <- function(df.true, df.pred, title = "", true.lab ="True Class", pred.lab ="Predicted Class",
                        high.col = 'red', low.col = 'white') {
  #convert input vector to factors, and ensure they have the same levels
  df.true <- as.factor(df.true)
  df.pred <- factor(df.pred, levels = levels(df.true))
  
  #generate confusion matrix, and confusion matrix as a pecentage of each true class (to be used for color) 
  df.cm <- table(True = df.true, Pred = df.pred)
  df.cm.col <- df.cm / rowSums(df.cm)
  
  #convert confusion matrices to tables, and binding them together
  df.table <- reshape2::melt(df.cm)
  df.table.col <- reshape2::melt(df.cm.col)
  df.table <- left_join(df.table, df.table.col, by =c("True", "Pred"))
  
  #calculate accuracy and class accuracy
  acc.vector <- c(diag(df.cm)) / c(rowSums(df.cm))
  class.acc <- data.frame(Pred = "Class Acc.", True = names(acc.vector), value = acc.vector)
  acc <- sum(diag(df.cm)) / sum(df.cm)
  
  #plot
  ggplot() +
    geom_tile(aes(x=Pred, y=True, fill=value.y),
              data=df.table, size=0.2, color=grey(0.5)) +
    geom_tile(aes(x=Pred, y=True),
              data=df.table[df.table$True==df.table$Pred, ], size=1, color="black", fill = 'transparent') +
    scale_x_discrete(position = "top",  limits = c(levels(df.table$Pred), "Class Acc.")) +
    scale_y_discrete(limits = rev(unique(levels(df.table$Pred)))) +
    labs(x=pred.lab, y=true.lab, fill=NULL,
         title= paste0(title, "\nAccuracy ", round(100*acc, 1), "%")) +
    geom_text(aes(x=Pred, y=True, label=value.x),
              data=df.table, size=4, colour="black") +
    geom_text(data = class.acc, aes(Pred, True, label = paste0(round(100*value), "%"))) +
    scale_fill_gradient(low=low.col, high=high.col, labels = scales::percent,
                        limits = c(0,1), breaks = c(0,0.5,1)) +
    guides(size=F) +
    theme_bw() +
    theme(panel.border = element_blank(), legend.position = "bottom",
          axis.text = element_text(color='black'), axis.ticks = element_blank(),
          panel.grid = element_blank(), axis.text.x.top = element_text(angle = 30, vjust = 0, hjust = 0)) +
    coord_fixed()

} 

You can just copy and paste the function, and save it to your global environment.

Here's an example:

mydata <- data.frame(true = c("a", "b", "c", "a", "b", "c", "a", "b", "c"),
                     predicted = c("a", "a", "c", "c", "a", "c", "a", "b", "c"))

conf_matrix(mydata$true, mydata$predicted, title = "Conf. Matrix Example")

Answer 4

You could use the function conf_mat() from yardstick plus autoplot() to get in a few rows a pretty nice result.

Plus you can still use basic ggplot sintax in order to fix the styling.

library(yardstick)
library(ggplot2)


# The confusion matrix from a single assessment set (i.e. fold)
cm <- conf_mat(truth_predicted, obs, pred)

autoplot(cm, type = "heatmap") +
  scale_fill_gradient(low="#D6EAF8",high = "#2E86C1")

---

Just as an example of further customizations, using ggplot sintax you can also add back the legend with:

+ theme(legend.position = "right")

Changing the name of the legend would be pretty easy too : + labs(fill="legend_name")

Data Example:

set.seed(123)
truth_predicted <- data.frame(
  obs = sample(0:1,100, replace = T),
  pred = sample(0:1,100, replace = T)
)
truth_predicted$obs <- as.factor(truth_predicted$obs)
truth_predicted$pred <- as.factor(truth_predicted$pred)

Answer 5

Here a simple ggplot2 based idea that can be changed as desired, I'm using the data from this link:

#data
confusionMatrix(iris$Species, sample(iris$Species))
newPrior <- c(.05, .8, .15)
names(newPrior) <- levels(iris$Species)

cm <-confusionMatrix(iris$Species, sample(iris$Species))

Now cm is a confusion matrix object, it's possible to take out something useful for the purpose of the question:

# extract the confusion matrix values as data.frame
cm_d <- as.data.frame(cm$table)
# confusion matrix statistics as data.frame
cm_st <-data.frame(cm$overall)
# round the values
cm_st$cm.overall <- round(cm_st$cm.overall,2)

# here we also have the rounded percentage values
cm_p <- as.data.frame(prop.table(cm$table))
cm_d$Perc <- round(cm_p$Freq*100,2)

Now we're ready to plot:

library(ggplot2)     # to plot
library(gridExtra)   # to put more
library(grid)        # plot together

# plotting the matrix
cm_d_p <-  ggplot(data = cm_d, aes(x = Prediction , y =  Reference, fill = Freq))+
  geom_tile() +
  geom_text(aes(label = paste("",Freq,",",Perc,"%")), color = 'red', size = 8) +
  theme_light() +
  guides(fill=FALSE) 

# plotting the stats
cm_st_p <-  tableGrob(cm_st)

# all together
grid.arrange(cm_d_p, cm_st_p,nrow = 1, ncol = 2, 
             top=textGrob("Confusion Matrix and Statistics",gp=gpar(fontsize=25,font=1)))

Answer 6

I really liked the beautiful confusion matrix visualization from @Cybernetic and made two tweaks to hopefully improve it further.

1) I swapped out the Class1 and Class2 with the actual values of the classes. 2) I replace the orange and blue colors with a function that generates red (misses) and green (hits) based on percentiles. The idea is to quickly see where the problems/successes are and their sizes.

Screenshot and code:

draw_confusion_matrix <- function(cm) {

  total <- sum(cm$table)
  res <- as.numeric(cm$table)

  # Generate color gradients. Palettes come from RColorBrewer.
  greenPalette <- c("#F7FCF5","#E5F5E0","#C7E9C0","#A1D99B","#74C476","#41AB5D","#238B45","#006D2C","#00441B")
  redPalette <- c("#FFF5F0","#FEE0D2","#FCBBA1","#FC9272","#FB6A4A","#EF3B2C","#CB181D","#A50F15","#67000D")
  getColor <- function (greenOrRed = "green", amount = 0) {
    if (amount == 0)
      return("#FFFFFF")
    palette <- greenPalette
    if (greenOrRed == "red")
      palette <- redPalette
    colorRampPalette(palette)(100)[10 + ceiling(90 * amount / total)]
  }

  # set the basic layout
  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 
  classes = colnames(cm$table)
  rect(150, 430, 240, 370, col=getColor("green", res[1]))
  text(195, 435, classes[1], cex=1.2)
  rect(250, 430, 340, 370, col=getColor("red", res[3]))
  text(295, 435, classes[2], 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=getColor("red", res[2]))
  rect(250, 305, 340, 365, col=getColor("green", res[4]))
  text(140, 400, classes[1], cex=1.2, srt=90)
  text(140, 335, classes[2], cex=1.2, srt=90)

  # add in the cm results
  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)
}

Answer 7

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:

Answer 8

You could use the built-in fourfoldplot. For example,

ctable <- as.table(matrix(c(42, 6, 8, 28), nrow = 2, byrow = TRUE))
fourfoldplot(ctable, color = c("#CC6666", "#99CC99"),
             conf.level = 0, margin = 1, main = "Confusion Matrix")