Alright, let's break this down. You've got two forecasting models where Model 1 has a lower MAE (797.95725) but Model 2 has a lower MAPE (203.40554)—totally normal to feel stuck here, since these metrics measure different aspects of prediction error. Let's walk through how to decide which one fits your use case better.

Key Factors to Guide Your Decision #

1. Understand the Core Difference Between MAE and MAPE #

• MAE (Mean Absolute Error) calculates the average absolute magnitude of errors, treating every error equally. A 10-unit error on a small actual value counts the same as a 10-unit error on a large one. It’s great for scenarios where fixed-size errors have consistent business impact.
• MAPE (Mean Absolute Percentage Error) normalizes each error by the actual value, focusing on relative error. A 10-unit error on a 100-unit actual is 10%, while on a 1000-unit actual it’s only 1%—so MAPE prioritizes proportional accuracy over absolute size.

2. Align with Your Business Priorities #

• If your goal is to minimize the total absolute gap between predictions and actuals (e.g., inventory management where overstocking by 50 units costs the same regardless of the product’s value), Model 1’s lower MAE is the better pick.
• If relative accuracy matters more (e.g., revenue forecasting where a 5% error on a $10k product is far more costly than a 15% error on a $100 product), Model 2’s lower MAPE is the right choice.

3. Analyze Your Data’s Distribution #

• MAPE can be skewed by small actual values: even a tiny absolute error becomes a huge percentage error when divided by a small number. Check your test set—if Model 2’s lower MAPE comes from avoiding extreme percentage errors on low-value points, but Model 1 performs better on high-impact, large-value data, Model 1 might still be preferable.
• If your data has a wide range of values and proportional error is a better proxy for real-world harm, stick with Model 2.

4. Dig Into Error Details (Don’t Stop at Aggregate Metrics) #

• Plot actual vs. predicted values for both models. Look for:
  • Which model performs better on your most critical data subsets (e.g., top-selling products, high-revenue periods)?
  • Is one model consistently biased (always over/under-predicting) while the other has random errors? Bias can often be corrected with post-processing, whereas random errors might be harder to mitigate.

5. Supplement with Additional Validation #

• Try other metrics like RMSE (which penalizes large errors more heavily) or Median Absolute Deviation (MAD) (more robust to outliers) to get another perspective.
• Run cross-validation or test on a separate holdout set to confirm if the metric trends hold across different data samples.

At the end of the day, there’s no universal "better" model—your choice should tie directly to what success means for your specific forecasting task. Focus on the real-world consequences of prediction errors, and you’ll make the right call.

内容的提问来源于stack exchange，提问作者Mostafa Kotb