This document discusses metrics for evaluating predictive performance in regression models. It explains that accuracy is used for classification models, while regression models are evaluated based on the errors between predicted versus true values. Specifically, mean absolute error, mean squared error, and root mean square error are introduced as common metrics, where errors are calculated as the absolute or squared differences between predictions and true values, and then averaged. Examples are provided to demonstrate calculating these error metrics.

1. AI Club

2. AI Club
Regression Metrics

3. How good a model is: Predictive performance
• Predictive performance in Classification: Accuracy
• Predictive performance in Regression ?

4. How good a model is: Predictive performance
• Predictive performance in Classification: Accuracy
• Predictive performance in Regression ?
• What can you use to tell you how good your regression AI is?

5. Predictive performance in regression
• Predictive performance in Classification: Accuracy
• Predictive performance in Regression:
• Calculate the error = (Predicted – True)
If the correct value is 10 and AI predicted 11
Error = 11-10 = 1

6. Predictive performance in regression
• Predictive performance in Classification: Accuracy
• Predictive performance in Regression:
• Calculate the error = (Predicted – True)
If the correct value is 10 and AI predicted 9
Error = ?

7. Predictive performance in regression
• Predictive performance in Classification: Accuracy
• Predictive performance in Regression:
• Calculate the error = (Predicted – True)
If the correct value is 10 and AI predicted 9
Error = 9-10 = -1?

8. Predictive performance in regression
• Predictive performance in Classification: Accuracy
• Predictive performance in Regression:
• Calculate the error = (Predicted – True)
If the correct value is 10 and AI predicted 9
Absolute Value Error = Absolute Value(-1) = 1

9. Predictive performance in regression
• Predictive performance in Classification: Accuracy
• Predictive performance in Regression:
• Calculate the error = (Predicted – True)
In case of accuracy, if the AI got 6 correct answers out of 10,
its accuracy = 60%
Similarly, if the AI got errors of 1.2,5.1,2,3,6,1,4.3.. etc for 10
questions, what is the overall error?

10. Predictive performance in regression
• Predictive performance in Classification: Accuracy
• Predictive performance in Regression:
• Calculate the error = (Predicted – True)
In case of accuracy, if the AI got 6 correct answers out of 10,
its accuracy = 60%
Similarly, if the AI got errors of 1.2,5.1,2,3,6,1,4.3.. etc for 10
questions, what is the overall error?
Over all error is Average(1.2,5.1,2,3,4,1,4.3 …)

11. Predictive performance in regression
• Exercise:
• True Value: 5, Predicted Value: 3
• True Value: 7, Predicted Value: 10
Predictive performance in regression

12. Predictive performance in regression
• Example:
• True Value: 5, Predicted Value: 3 -> Error: 2
• True Value: 7, Predicted Value: 10 -> Error: -3
Predictive performance in regression

13. Predictive performance in regression
• Example:
• True Value: 5, Predicted Value: 3 -> Absolute Error: 2
• True Value: 7, Predicted Value: 10 -> Absolute Error: 3
Predictive performance in regression

14. Predictive performance in regression
• Example:
• True Value: 5, Predicted Value: 3 -> Absolute Error: 2
• True Value: 7, Predicted Value: 10 -> Absolute Error: 3
• Mean Absolute Error: (3+2)/2
Predictive performance in regression

15. Predictive performance in regression
• Example:
• True Value: 5, Predicted Value: 3 -> Absolute Error: 2
• True Value: 7, Predicted Value: 10 -> Absolute Error: 3
• Mean Absolute Error: (3+2)/2
• Another way to measure error
• True Value: 5, Predicted Value: 3 -> Square Error: 4
• True Value: 7, Predicted Value: 10 -> Square Error: 9
Predictive performance in regression

16. Predictive performance in regression
• Example:
• True Value: 5, Predicted Value: 3 -> Absolute Error: 2
• True Value: 7, Predicted Value: 10 -> Absolute Error: 3
• Mean Absolute Error: (3+2)/2
• Another way to measure error
• True Value: 5, Predicted Value: 3 -> Square Error: 4
• True Value: 7, Predicted Value: 10 -> Square Error: 9
• Mean Square Error: (9+4)/2
Predictive performance in regression

17. Predictive performance in regression
• Example:
• True Value: 5, Predicted Value: 3 -> Absolute Error: 2
• True Value: 7, Predicted Value: 10 -> Absolute Error: 3
• Mean Absolute Error: (3+2)/2
• Another way to measure error
• True Value: 5, Predicted Value: 3 -> Square Error: 4
• True Value: 7, Predicted Value: 10 -> Square Error: 9
• Mean Square Error: (9+4)/2
• Root Mean Square Error: root((9+4)/2)
Predictive performance in regression

18. Summary: How to Measure Regression
• In regression, you have to compare the number you get from
the AI with the correct number
• Lower difference is better
• Its called Root Mean Square Error (RMSE)
• Take the difference (Prediction – Right answer)
• Square it so that it is always positive (Prediction – Right Answer) *
(Prediction – Right Answer)
• Take the average (Mean)
• Take the square root

19. THANK YOU
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