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Author: Lifeeng Wei

articles/machine-learning/how-to-understand-automated-ml.md

@@ -207,6 +207,9 @@ Automated ML normalizes regression and forecasting metrics which enables compari
 
 While there is no standard method of normalizing error metrics, automated ML takes the common approach of dividing the error by the range of the data: `normalized_error = error / (y_max - y_min)`
 
+>[!Note]
+>The range of data is not saved with the model. If you do inference with the same model on a holdout test set, `y_min` and `y_max` may change according to the test data and the normalized metrics may not be directly used to compare the model's performance on training and test sets. You can pass in the value of `y_min` and `y_max` from your training set to make the comparison fair.
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 When evaluating a forecasting model on time series data, automated ML takes extra steps to ensure that normalization happens per time series ID (grain), because each time series likely has a different distribution of target values.
 ## Residuals