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L1 Loss (Absolute Loss or Mean Absolute Error (MAE)) - Python Automation and Machine Learning for ICs - - An Online Book - |
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| Python Automation and Machine Learning for ICs http://www.globalsino.com/ICs/ | ||||||||
| Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix | ||||||||
================================================================================= The L1 loss function, also known as the mean absolute error (MAE), is a mathematical measure of the average absolute differences between predicted and actual values. It is commonly used in regression analysis and machine learning to assess the accuracy of a model's predictions. For a set of n data points, the L1 loss is calculated by taking the sum of the absolute differences between the predicted values (ลท) and the actual values (y):
In other words, it computes the average absolute difference between the predicted and actual values. The L1 loss is less sensitive to outliers compared to the L2 loss (mean squared error), making it more robust in the presence of data with extreme values. The formula uses the absolute value function to ensure that the differences are positive and the overall loss is not biased by the direction of the errors. ============================================
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