### Attractive Confusion Matrices in R Plotted with fourfoldplot

As part of writing analyses using neural networks I thought of displaying a confusion matrix, but went looking for something more. What I found was either ugly and simple, or attractive but complicated. The following code demonstrates using fourfoldplot, with original insight gained from fourfoldplot: A prettier confusion matrix in base R, and a great documentation page, R Documentation's page for fourfoldplot.

The code is below, as is the related graph. Source data can be found here.

The code is below, as is the related graph. Source data can be found here.

```
###############################################
# Load and clean data
################################################
Politics.df <- read.csv("BigFiveScoresByState.csv", na.strings = c("", "NA"))
Politics.df <- na.omit(Politics.df)
################################################
# Neural Net with nnet and caret
################################################
# load packages
library(nnet)
library(caret)
# set equation
equation <- as.formula("Liberal ~ Openness + Conscientiousness + Extraversion + Neuroticism + Agreeableness")
# train, predict, combine
Politics.model <- train(equation, Politics.df, method = 'nnet', linout = TRUE, trace = FALSE)
Politics.model.predicted <- predict(Politics.model, Politics.df)
Politics.model.combined <- cbind(Politics.model.predicted, Politics.df)
################################################
# Plot with FourFoldPlot
################################################
# create the confusion matrix
Politics.model.matrix <- confusionMatrix(Politics.model.combined$predictionBinary, Politics.model.combined$Liberal)
# set the column and row names
colnames(Politics.model.matrix$table) <- c("Conservative", "Liberal")
rownames(Politics.model.matrix$table) <- c("Conservative", "Liberal")
# generate the plot
fourfoldplot(Politics.model.matrix$table, color = c("Red", "Green"), conf.level = 0, margin = 1, main = "Confusion Matrix for Model")
```