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MAPE vs MAE Comparison

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Basic Concepts: MAPE and MAE measure regression model accuracy using predicted and actual values
Both metrics quantify average deviation between actual and predicted values
MAPE shows percentage difference, MAE uses absolute difference
Advantages and Limitations: MAE works well for models with same range, MAPE better for different ranges
MAE treats extreme values as normal, MAPE more susceptible to extreme values
MAPE is asymmetric, MAE remains constant regardless of range
MAPE division by zero causes division error, MAE avoids this
Practical Considerations: MAPE values can be skewed by extreme values
MAE values are more evenly distributed across range
Rankings of MAPE and MAE scores may not be consistent across markets
Both metrics can be used together for comprehensive analysis