One-vs-rest and one-vs-one strategies in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the One-vs-Rest (OvR) strategy in multi-class classification?

One-vs-Rest trains one classifier per class, where each classifier learns to separate that class from all other classes combined.

beginner

How does the One-vs-One (OvO) strategy work?

One-vs-One trains a classifier for every pair of classes, so each classifier distinguishes between two classes only.

intermediate

Which strategy usually requires more classifiers: One-vs-Rest or One-vs-One?

One-vs-One requires more classifiers because it trains one classifier for every pair of classes, which grows roughly with the square of the number of classes.

intermediate

Why might One-vs-One classifiers be faster to train individually than One-vs-Rest classifiers?

Because each One-vs-One classifier only deals with two classes, the training data is smaller and simpler, making training faster per classifier.

intermediate

How are predictions combined in One-vs-Rest and One-vs-One strategies?

In One-vs-Rest, the class with the highest confidence score wins. In One-vs-One, a voting system counts which class wins most pairwise classifiers.

In One-vs-Rest, how many classifiers are trained for a problem with 4 classes?

A 4

B 6

C 12

D 1

One-vs-Rest trains one classifier per class, so 4 classes mean 4 classifiers.

What is the number of classifiers needed in One-vs-One for 4 classes?

A 4

B 8

C 6

D 12

One-vs-One trains classifiers for every pair of classes: 4 choose 2 = 6.

Which strategy is simpler to implement for multi-class classification?

A One-vs-Rest

B One-vs-One

C Both are equally complex

D Neither is used for multi-class

One-vs-Rest is simpler because it trains fewer classifiers and uses a straightforward approach.

How does One-vs-One decide the final class prediction?

A Highest confidence score from one classifier

B Average of all classifier outputs

C Random selection

D Majority vote from all pairwise classifiers

One-vs-One uses majority voting among all pairwise classifiers to decide the final class.

Which strategy might be better when classes are very imbalanced?

A One-vs-Rest

B One-vs-One

C Both perform equally

D Neither works with imbalance

One-vs-One can handle imbalance better because each classifier focuses on two classes only.

Explain the difference between One-vs-Rest and One-vs-One strategies in multi-class classification.

Think about how many classifiers each strategy trains and how they decide the final class.

Describe a situation where One-vs-One might be preferred over One-vs-Rest.

Consider training speed and class balance.