👉 The Mean Absolute Error (MAE) is a statistical measure used to evaluate the accuracy of predictions by quantifying the average magnitude of errors in a set of predictions, without considering their direction. It calculates the average of the absolute differences between predicted values and actual values, providing a straightforward interpretation of how far off the predictions are on average. Unlike Mean Squared Error (MSE), which squares these differences and gives more weight to larger errors, MAE treats all errors equally, making it a robust metric for understanding prediction accuracy in scenarios where the cost of errors is uniform. This simplicity and interpretability make MAE a popular choice in various fields, including finance, engineering, and data science.
