Variants of Multiclass Classification: One-vs-One and One-vs-All:

In multiclass classification, where there are more than two classes, two common strategies for training binary classifiers are the one-vs-one (OvO) and one-vs-all (OvA) approaches.

  1. One-vs-One (OvO):
    • Approach:
      • For each pair of classes, a binary classifier is trained. This results in C×(C−1)/2 classifiers for C classes.
      • During prediction, each classifier votes for a class, and the class with the most votes across all classifiers is chosen as the final prediction.
    • Advantages:
      • The advantage of OvO is that it only trains on the data relevant to the two classes being compared, making it efficient for large datasets.
      • It is particularly useful when training a binary classifier is fast and when memory usage is a concern.
    • Disadvantages:
      • It might lead to ties in the voting process, especially if there are more than two classes, which requires additional handling.
  2. One-vs-All (OvA or One-vs-Rest):
    • Approach:
      • For each class, a binary classifier is trained to distinguish that class from the rest of the classes.
      • During prediction, all classifiers are used, and the class associated with the classifier that outputs the highest confidence is chosen as the final prediction.
    • Advantages:
      • OvA is often computationally more efficient than OvO, especially when there are a large number of classes.
      • It avoids tie issues since each instance is assigned to only one class.
    • Disadvantages:
      • It can lead to imbalanced datasets for each binary classifier, especially if some classes have significantly more instances than others.