AI Engineering Degree Practice Exam

The one-vs-all classification approach, which involves training a separate binary classifier for each class in a multi-class problem, has a notable disadvantage in the form of creating ambiguous regions where multiple classes can be valid outputs. This occurs because each classifier is trained independently; therefore, during inference, it is possible for two or more classifiers to show a high level of confidence in their respective classes simultaneously. This overlap can lead to confusion in determining which class should be assigned to a new instance, especially in cases where the decision boundaries of the classifiers get too close to one another. While options discussing computational expense or the number of classes may address other concerns associated with this approach, they do not capture the essence of ambiguity in class assignment as effectively. This aspect of ambiguity makes it critical to consider the decision boundaries between classes in a one-vs-all setup. Thus, the creation of overlapping decision regions highlights a significant limitation inherent to this methodology.