QDA | lengths of training and test data sets | How to split data in training and test data? - r

Question

In QDA (Quadratic Discriminant Analysis), do i need to keep length of training and test data exactly same? If not, how do you find a Confusion Matrix in such cases?
Here's psuedo data.
Because if I keep training-data and test data sets of different lengths, it gives an error (Using R Studio):
"Error in table(pred, true) : all arguments must have the same length".
Tried to remove NAs using na.omit() on both data sets as well as pred and true; and using na.action = na.exclude for qda(), but it didn't work.
After dividing the data set in exactly half; half of it as training and half as test; it worked perfectly after na.omit() on pred and true.
Following is the code used for either of approaches. In approach 2, with data split into equal halves, it worked perfectly fine.
#Approach 1: divide data age-wise
train <- vif_data$Age < 30
# there are around 400 values passing (TRUE) above condition and around 50 failing (FALSE)
train_vif <- vif_data[train,]
test_vif <- vif_data[!train,]
#taking QDA
zone_qda <- qda(train_vif$Awareness~train_vif$Zone, na.action = na.exclude)
#compare QDA against test data
zone_pred <- predict(zone_qda, test_vif)
#omitting nulls
pred <- na.omit(zone_pred$class)
true <- na.omit(test_vif$Awareness)
length(pred) # result: 399
length(true) # result: 47
#that's where it throws error: "Error in table(zone_pred$class, train_vif) : all arguments must have the same length"
zone_aware <- table(zone_pred$class, train_vif)
# OR
zone_aware <- table(pred, true)
accur <- mean(zone_pred$class==test_vif$Awareness)
###############################
#Approach 2: divide data into random halves
train <- splitSample(dataset = vif_data, div = 2, path = "./", type = "csv")
train_data <- read.csv("splitSample_s1.csv")
test_data <- read.csv("splitSample_s2.csv")
#taking QDA
zone_qda <- qda(train_vif$Awareness~train_vif$Zone, na.action = na.exclude)
#compare QDA against test data
zone_pred <- predict(zone_qda, test_vif)
#omitting nulls
pred <- na.omit(zone_pred$class)
true <- na.omit(test_vif$Awareness)
length(train_vif)
# this works fine
zone_aware <- table(zone_pred$class, train_vif)
# OR
zone_aware <- table(pred, true)
accur <- mean(zone_pred$class==test_vif$Awareness)
Want to know if there is any method by which we can have a confusion matrix with data set unequally divided into training and test data set.
Thanks!

Answer 1

Are you plugging in your training inputs instead of your test set input data to predict? Notice how this yields the same error message:
table(c(1,2),c(1,2,3))
If pred isn't the right length, then you're probably predicting incorrectly. At the moment, you haven't shared any of your code, so I cannot say anything more. But there is no reason that you shouldn't be able to get a confusion matrix using test data of different size than your training data.