I was wondering what are good ways to visualize the errors made by a multinomial logistic regression model?
I can compute the class probabilities and I have the a test set with the real classes and a vector of predicted classes. What are the most useful ways to visualize the performance of my model?
In addition, I was wondering whether you can calculate some kind of residual out of it. If so, should I then use the class probabilities to calculate the residual or the predictions (as in just picking the class with the highest probability). In order to clarify my statement consider the fact that I have 3 classes.
If I use class probability to construct some kind of residual, for the first observation I would predict the following class probabilities. Given that this observation is of class A, we then get a residual of:
(0.25-1)^2 + (0.33-0)^2 + (0.42-0)^2
A: 0.25 B: 0.33 C: 0.42
However if I use the predictions I will get:
(0-1)^2 + (0-0)^2 + (1-0)^2
What would be a better option and what are good informative graphs for multinomial models?
I have already created a confusion matrix with a heatmap.
Code for rolando2. This is what I meant with a confusion matrix with heatmap.
library(ggplot2) library(data.table) # Generating fake confusion matrix dt <- data.table(Prediction = c("A", "A", "B", "B"), Reference = c("A", "B", "A", "B"), Value = c(10, 11, 15, 20)) ggplot(dt, aes(x = Prediction, y = Reference)) + geom_tile(aes(fill = Value)) + theme_bw() + geom_text(aes(x = Prediction, y = Reference, label = Value), colour = "white")