Confusion Matrix for Multiple Regression Model

Hey, I am running a Multiple regression model. And my aim is to get the confusion Matrix for the below model but, it gives me an error.

Here is the code:

Loading the data

Train_data = DATA_Pressure_Depletion
Test_data = Test1

# Checking the summary of the data
summary(Train_data)
summary(Test_data)
# Plotting the Missmap of the data.
missmap(Train_data)
missmap(Test1)

# Multiple regression Model

model_reg <- lm(`Experimental AOP`~., data = Train_data)
summary(model_reg)

# Prediction
predict_1 <- predict(model_reg, newdata = Test_data)
confusionMatrix(predict_1, Test_data$`Experimental AOP`)

Error:
Error: dataandreference should be factors with the same levels.

Data for Train:

Temp Pb Experimental AOP
210.2 3221 6854
219.2 3283 6587
230 3276 6419
240 3289 6225
212 4259 5299
289 4283 7300
240 4150 7550
289 4843 6900
248 4617 8500
208 2420 4600
150 2128 5050
100 1850 6700
208 3617 5200
150 3460 6300
100 3565 8500
271 2902 4500
182 2568 4200
94 1922 3700
284 2692 5005
248 2480 5228
194 2377 5511
167 2027 5600
284 2940 5398
248 2850 6607
178 2620 6874
144 2435 8705
275 3720 4967
235 3583 5143
194 3413 5555

Test data:

Temp Pb Experimental AOP
254.93 2300 3755.5
244.13 1900 3552.5
230 2400 3799
236 2500 3944
190.13 4263 5800
190 2500 5400
230 2700 4050
260 2900 3650
240 3059 4513
230 3060 4600
240 3064 4405
230 3150 4640

Your data looks continuous - I believe a confusion matrix is only appropriate for classification problems. For example, how would you determine whether a continuous prediction is a true positive or a false positive? Unless I am grossly misunderstanding, I think the solution to your problem is to use another method for evaluating your model (RMSE, MAE, etc.)

Thanks a lot, dear. RMSE and other accuracy measures would be perfect.