One of the most important steps in building a model is to check how accurately the model predicts outcomes. Today, we are inviting Data Analytics from SCB TechX, Khun Golf Mathee Prasertkijaphan, to share how to measure the performance of a Regression Model using 3 metrics: MAE, MSE, and RMSE (Please refer to the images below for more details.) as follows:
- MAE (Mean Absolute Error) calculates the average absolute difference between the predicted and actual values. This method is better for evaluating model performance when the data used for calculation has Outliers.
- MSE (Mean Squared Error) calculates the average of the squared differences between predicted and actual values. These methods are good for evaluating model performance when there are few Outliers.
- RMSE (Root Mean Squared Error) calculates the Square Root of the above MSE metric. However, it has the advantage of being easier to interpret as it uses the same performance unit as the data since there is no influence of exponentials as with MSE.
In measuring the accuracy of the model, If the value is close to zero, it indicates the model is highly accurate in prediction. However, the chance of getting a value of 0 is very low. Therefore, try testing the model with approximately two sets of prepared data. If the value decreases continuously, it shows that the model’s predictions are accurate and can be utilized.
Lastly, SCB TechX is ready to provide any organization with professional advice, technology solutions, and comprehensive Data Platform services through TechX Data & AI Solutions.
If you are interested, please feel free to contact us at contact@scbtechx.io
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