About this document

Understanding Mean Absolute Error (MAE) by akashvardhan82 is a document available to read on EtoBox.

Mean Absolute Error (MAE) measures the average absolute difference between actual and predicted values, calculated by summing the absolute errors and dividing by the number of observations. MAE is advantageous because it is in the same unit as the output variable and is robust to outliers, but it has the disadvantage of being non-differentiable, requiring the use of optimizers like Gradient Descent. The document also promotes a Summer Training program for further learning about MAE and machine learning inte

Author

akashvardhan82

Language

EN