# how to compute mean average precision

The equation for computing the mean average precision (MAP) is shown below:MAP (Q) = 1 |Q |鈭?j = 1 |Q |1 m j 鈭?k = 1 m j Precision (R j k)The inner summation is something that we need to focus on. The outer summation is simply an average over the queries of the inner sum.

• ### How do you calculate average precision from prediction scores?

• Compute average precision (AP) from prediction scores. AP summarizes a precision-recall curve as the weighted mean of precisions achieved at each threshold, with the increase in recall from the previous threshold used as the weight: AP = 鈭?n ( R n 鈭?R n 鈭?1) P n. where P n and R n are the precision and recall at the nth threshold [1].

• ### How is the average precision of a model calculated?

• Using different thresholds, a precision-recall curve is created. From that curve, the average precision (AP) is measured. For an object detection model, the threshold is the intersection over union (IoU) that scores the detected objects. Once the AP is measured for each class in the dataset, the mAP is calculated.

• ### What is mean average precision (map)?

• Since you’re reading this you’ve probably just encountered the term Mean Average Precision, or MAP. This is a very popular evaluation metric for algorithms that do information retrieval, like google search.

• ### How is average precision measured in machine learning?

• Using different thresholds, a precision-recall curve is created. From that curve, the average precision (AP) is measured. For an object detection model, the threshold is the intersection over union (IoU) that scores the detected objects.