Recall (haha pun) that the formula for recall is $R = TP / (TP + FN)$. To compute the macro-average recall of a multi-class classification, compute $R$ for each class, add them together, and then divide by the number of classes. We can summarize this as the following formula:

macro-average recall = $(\sum_{i=1}^nR_i) / n$, where $R_i$ is the recall of the $i$th class and there are $n$ classes.

University of Michigan - Ann Arbor

Macro-average is the average of some metric (e.g. precision or recall) among classes, where all classes are weighted equally.  Since this effectively gives a "boost" to smaller classes, macro-averages are more indicative of the precision/recall within smaller classes than within larger classes. According to the referenced article, however, macro-averaging can be bad practice when classes vary widely in their number of instances.

Macro-average

https://datascience.stackexchange.com/questions/15989/micro-average-vs-macro-average-performance-in-a-multiclass-classification-settin

Micro Average vs Macro average Performance in a Multiclass classification setting

Recall that the formula for precision is $Pr = TP / (TP + FP)$. To compute the macro-average precision of a multi-class classification, compute $Pr$ for each class, add them together, and then divide by the number of classes. We can summarize this as the following formula:

macro-average precision = $(\sum_{i=1}^nPr_i) / n$, where $Pr_i$ is the precision of the $i$th class and there are $n$ classes.

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