Absolute error measures the difference between an estimated value and its corresponding true value without considering directionality. It gives the magnitude of the discrepancy between the two values.
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Relative Error: Relative error compares the absolute error to the true value, giving a measure of how large the error is relative to the size of the true value.
Mean Absolute Error: Mean absolute error calculates the average absolute difference between estimated and true values over multiple data points.
Percent Error: Percent error expresses the absolute error as a percentage of the true value, providing a standardized way to compare errors across different scales.