Exam DP-100 topic 3 question 140 discussion

I think it is correct ChatGPT-3.5 answer To avoid labeling negative samples as positive and handle class imbalance, you can use the following configurations: Metric type: Area Under the Precision-Recall Curve (AUPRC) or Average Precision Score (AP). These metrics are suitable for imbalanced classification tasks as they focus on the trade-off between precision and recall, instead of relying solely on accuracy. They are more robust when dealing with class imbalance. Averaging method: Macro Macro averaging calculates the metric independently for each class and then takes the average across all classes. It treats each class equally, regardless of the class distribution. It can be useful when you want to evaluate the model's performance across all classes without bias towards the majority class.

Metric type: precision Precision is the ability of a model to avoid labeling negative samples as positive. reference: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml?view=azureml-api-2#classification-metrics Averaging method: macro While each averaging method has its benefits, one common consideration when selecting the appropriate method is class imbalance. 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. reference: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml?view=azureml-api-2#classification-metrics

Metric Type: Precision Averaging Method: Micro Micro- and macro-averages (for whatever metric) will compute slightly different things, and thus their interpretation differs. A macro-average will compute the metric independently for each class and then take the average (hence treating all classes equally), whereas a micro-average will aggregate the contributions of all classes to compute the average metric. In a multi-class classification setup, micro-average is preferable if you suspect there might be class imbalance (i.e you may have many more examples of one class than of other classes). SaM

I am not sure about the averaging method. Macro just takes the mean over all classes whereas micro incorporates class contribution.