What is a mean average precision at k?
The statistical mean of all average precision at k scores across a validation dataset.
mean average precision at k explained in plain English
The statistical mean of all average precision at k scores across a validation dataset. One use of mean average precision at k is to judge the quality of recommendations generated by a recommendation system. Although the phrase "mean average" sounds redundant, the name of the metric is appropriate. After all, this metric finds the mean of multiple average precision at k values.
Suppose you build a recommendation system that generates a personalized list of recommended novels for each user. Based on feedback from selected users, you calculate the following five average precision at k scores (one score per user): - 0.73 - 0.77 - 0.67 - 0.82 - 0.76 The mean Average Precision at K is therefore:
Example
Practitioners refer to mean average precision at k when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.
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