We can compute micro-average precision by dividing the number of true positives across all classes by the sum of true positives and false positives across all classes. More formally, let $TP_i$ and $FP_i$ be the number of true positives and false positives, respectively, in class $i$. Given a multi-class classification with $n$ classes, we can express micro-average precision as the following formula: $\frac{\sum_{i=1}^n TP_i}{\sum_{i=1}^n (TP_i+FP_i)}$