When working on a machine learning project, choosing the right error or evaluation metric is critical. This is a measure of how well your model performs at the task you built it for, and choosing the correct metric for the model is a critical task for any machine learning engineer or data scientist. Mean Absolute Precision @k is used for recommendation problems.

For Zindi competitions, we choose the evaluation metric for each competition based on what we want the model to achieve. Understanding each metric and the type of model you use each for is one of the first steps towards mastery of machine learning techniques.

Mean Absolute Precision @k is a metric that evaluates the precision of a model's predictions within the top k items of a ranked list. It measures how well the model identifies relevant items by considering both the presence and the order of the items. The metric is computed by averaging the precision values of different users or queries, resulting in a comprehensive measure of the model's performance.

This is particularly important in recommendation systems, where the order of recommendations greatly affects user experience and the likelihood of engagement.

MAP@k allows flexibility in choosing the value of k based on the specific requirements of the application. For instance, if a recommendation system displays a fixed number of items to the user, evaluating precision at that specific number (k) provides a meaningful measure of performance. This adaptability makes MAP@k applicable to a wide range of scenarios.

When the primary objective is to optimize user satisfaction and engagement, MAP@k becomes a valuable metric. By considering the precision of recommendations within the top k items, MAP@k provides a user-centric evaluation, emphasizing the importance of accurate and relevant suggestions that align with user preferences.

MAP@k places importance on the order of relevant items within the ranked list. While this aspect can be advantageous, it may also introduce subjectivity or additional complexity, especially when determining the precise relevance order. Careful consideration should be given to the ranking algorithm to ensure its relevance and alignment with user preferences.

With this knowledge, you should be well equipped to use Mean Absolute Precision @k for your next machine learning project.

Why don’t you test out your new knowledge on one of our past competitions that uses MAP@k as its evaluation metric? We suggest the Turtle Recall: Conservation Challenge.