update metrics docs

Author: Lifeeng Wei

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

@@ -86,6 +86,9 @@ weighted_accuracy|Weighted accuracy is accuracy where each sample is weighted by
 
 Automated ML automatically detects if the data is binary and also allows users to activate binary classification metrics even if the data is multiclass by specifying a `true` class. Multiclass classification metrics will be reported no matter if a dataset has two classes or more than two classes. Binary classification metrics will only be reported when the data is binary, or the users activate the option. 
 
+> [!Note]
+> When a binary classification task is detected, we use `numpy.unique` to find the set of labels and the later label will be used as the `true` class. Since there is a sorting procedure in `numpy.unique`, the choice of `true` class will be stable.
+
 Note that multiclass classification metrics are intended for multiclass classification. When applied to a binary dataset, these metrics won't treat any class as the `true` class, as you might expect. Metrics that are clearly meant for multiclass are suffixed with `micro`, `macro`, or `weighted`. Examples include `average_precision_score`, `f1_score`, `precision_score`, `recall_score`, and `AUC`. For example, instead of calculating recall as `tp / (tp + fn)`, the multiclass averaged recall (`micro`, `macro`, or `weighted`) averages over both classes of a binary classification dataset. This is equivalent to calculating the recall for the `true` class and the `false` class separately, and then taking the average of the two.
 
 Besides, although automatic detection of binary classification is supported, it is still recommended to always specify the `true` class manually to make sure the binary classification metrics are calculated for the correct class.