AUC_micro, computed by counting the total true positives, false negatives, and false positives.AUC_macro, the arithmetic mean of the AUC for each class.MetricĪUC is the Area under the Receiver Operating Characteristic Curve. Refer to image metrics section for additional details on metrics for image classification models. For more detail, see the scikit-learn documentation linked in the Calculation field of each metric. ![]() The following table summarizes the model performance metrics that automated ML calculates for each classification model generated for your experiment. Learn more about binary vs multiclass metrics in automated ML. If classes have different numbers of samples, it might be more informative to use a macro average where minority classes are given equal weighting to majority classes. While each averaging method has its benefits, one common consideration when selecting the appropriate method is class imbalance.
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