one-vs.-all
Given a classification problem with N classes, a solution consisting of N separate binary classification model—one binary classification model for each possible outcome.
Plain English Explanation
Given a classification problem with N classes, a solution consisting of N separate binary classification model—one binary classification model for each possible outcome. For example, given a model that classifies examples as animal, vegetable, or mineral, a one-vs.-all solution would provide the following three separate binary classification models: - animal versus not animal - vegetable versus not vegetable - mineral versus not mineral
How is it used?
Practitioners refer to one-vs.-all when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.