Confusion Matrix for Multiple Regression Model

[TalhaAsif]

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

[dvetsch75]

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.)

[TalhaAsif]

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