📕 subnode [[@KGBicheno/mean_absolute_error_(mae)]]
in 📚 node [[mean_absolute_error_(mae)]]
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garden/KGBicheno/Artificial Intelligence/Introduction to AI/Week 3 - Introduction/Definitions/Mean_Absolute_Error_(Mae).md by @KGBicheno
Mean Absolute Error (MAE)
Go back to the [[AI Glossary]]
An error metric calculated by taking an average of absolute errors. In the context of evaluating a model’s accuracy, MAE is the average absolute difference between the expected and predicted values across all training examples. Specifically, for n examples, for each value y and its prediction y-hat, MAE is defined as follows:
📖 stoas
- public document at doc.anagora.org/mean_absolute_error_(mae)
- video call at meet.jit.si/mean_absolute_error_(mae)