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Micro-recall measures the recall of the aggregated contributions of all classes. It’s short for micro-averaged recall.
Micro-recall = 1 means the model’s predictions are perfect, all truly positive samples was predicted as the positive class.
Emphasis on common classes
Micro-averaging will put more emphasis on the common classes in the data set. This may be the preferred behavior for multi-label classification problems. Labels that are very rare in the dataset, e.g., a genre that only represents 0.01% of the data examples, shouldn’t influence the overall recall metric heavily if the model is performing well on the other more common genres.
Recall is a metric used in binary classification problems to answer the following question: What proportion of actual positives was predicted correctly?
Recall is defined as:
True positive is when actual positive is predicted positive, and
False negative is when actual positive is predicted negative.
Read more about this in the Confusion matrix entry in the glossary.
Note that you can always check the recall for each individual class in the Confusion matrix on the Evaluation view.
Micro-averaging is used when a problem has more than 2 labels that can be true, for example, in our tutorial Build your own music critic.
Micro-averaging is performed by first calculating the sum of all true positives and false positives, over all the classes. Then we compute the recall for the sums.
Micro-recall values can be high even if the model is performing very poorly on a rare class since it gives more weight to the common classes.
For single-label multi-class problems, micro-averaging would result in recall being exactly the same as accuracy. That does not provide any additional information about the model’s performance.
Let’s imagine you have a multi-class classification problem with 3 classes (A, B, C). The first step is to calculate how many True positives (TP) and False positives (FP) we have for each class:
A: 2 TP and 8 FP
B: 1 TP and 5 FP
C: 1 TP and 3 FP
Then we aggregate all classes:
TPsum: 2 + 1 + 1 = 4
FPsum: 8 + 5 + 3 = 20
And finally we calculate the recall of the aggregated values: