Final answer:
Instances in a ranking list are sorted by descending order of their predicted scores. This is commonly used in applications such as recommendation engines or risk assessments, where the highest scores indicate top priority or likelihood.
Step-by-step explanation:
When creating a ranking list, instances should be sorted by descending order of their predicted score.Creating a ranking list typically involves organizing instances based on their predicted scores from a model. In most cases, you will sort the instances by their predicted score in descending order, meaning the instance with the highest predicted score is placed at the top of the list, followed by the next highest, and so on. This helps in prioritizing instances based on the likelihood or degree of the predicted outcome. For example, in a recommendation engine, items are often ranked from most to least recommended based on their scores.
Descending order is the standard sorting mechanism when the goal is to identify the top instances, whether they are recommendations, risks, probabilities, or any other predictive metric.When creating a ranking list, it is common practice to sort the instances and their predicted scores in descending order. This means that the instance with the highest predicted score will be at the top of the list, followed by instances with lower scores. This allows for easy identification of the highest performing instances.For example, if you have a list of students and their predicted test scores, you would sort the students based on their predicted scores in descending order. This way, the student with the highest predicted score would be ranked first, and the student with the lowest predicted score would be ranked last.