Unable to plot my QDA classification method - r

Question

I have created a few types of classification, but when performing QDA I'm am unable to plot my predictions, does anyone know how to predict this?
set.seed(20220719)
#splitting training and testing data
ii = createDataPartition(classification[,3], p = .75, list = F)
training = classification[ii, ] #predictors for training
testing = classification[-ii, ] #predictors for testing
#fitting the model
qda_mod = qda(Group ~., training)
#predicting testing data
p2 = predict(qda_mod, testing)$class
tab1 = table(Predicted = p2, Actual = testing$Group)
tab1
data snipet
classification
X1 X2 Group
1 -1.007927175 0.399027128 0
2 -0.472479667 0.839121791 1
3 0.745229326 -1.279741933 1
4 -0.597907906 -1.942435976 1
5 0.186984091 -1.541910328 1
6 -0.395736986 -0.120650487 1
7 -0.155861012 1.193432933 0
8 0.382043985 -1.700433181 1
9 0.684346226 -0.890674936 1
10 0.453268993 0.674205724 1
Looking for an output similar to;

Answer 1

The Predicted groups have been colored with actual classification with red and green color. These are extracted from your predict call. The mix of red and green colors in groups shows the incorrect classification prediction. You can use the following code:
library(caret)
library(MASS)
set.seed(20220719)
#splitting training and testing data
ii = createDataPartition(classification[,3], p = .75, list = F)
training = classification[ii, ] #predictors for training
testing = classification[-ii, ] #predictors for testing
#fitting the model
qda_mod = qda(Group ~., training)
#predicting testing data
p2 = predict(qda_mod, testing)
#tab1 = table(Predicted = p2, Actual = testing$Group)
#tab1
# plot
par(mfrow=c(1,1))
plot(p2$posterior[,2], p2$class, col=testing$Group+10)
Output:
For extra info check this link.
#Oliver, made some nice functions in this post. Maybe this helps:
decisionplot <- function(model, data, class = NULL, predict_type = "class",
resolution = 100, showgrid = TRUE, ...) {
if(!is.null(class)) cl <- data[,class] else cl <- 1
data <- data[,1:2]
k <- length(unique(cl))
plot(data, col = as.integer(cl)+1L, pch = as.integer(cl)+1L, ...)
# make grid
r <- sapply(data, range, na.rm = TRUE)
xs <- seq(r[1,1], r[2,1], length.out = resolution)
ys <- seq(r[1,2], r[2,2], length.out = resolution)
g <- cbind(rep(xs, each=resolution), rep(ys, time = resolution))
colnames(g) <- colnames(r)
g <- as.data.frame(g)
### guess how to get class labels from predict
### (unfortunately not very consistent between models)
p <- predict(model, g, type = predict_type)
if(is.list(p)) p <- p$class
p <- as.factor(p)
if(showgrid) points(g, col = as.integer(p)+1L, pch = ".")
z <- matrix(as.integer(p), nrow = resolution, byrow = TRUE)
contour(xs, ys, z, add = TRUE, drawlabels = FALSE,
lwd = 2, levels = (1:(k-1))+.5)
invisible(z)
}
decisionplot(qda_mod, training, class = "Group")
Output: