Question

If there are n ( >2 ) classes how many classifiers would you have to train in One vs all classification ?

Answer 1

Explanation:

The One-vs-Rest strategy splits a multi-class classification into one ... ( also called one-vs-all), in which we train C binary classifiers, ...

Answer 2

One-vs-rest (OvR for a brief, also mentioned as One-vs-All or OvA) may be a heuristic method for using binary classification algorithms for multi-class classification.

Explanation:

• One-vs-rest (OvR for a brief, also mentioned as One-vs-All or OvA) may be a heuristic method for using binary classification algorithms for multi-class classification.
• It includes splitting of the multi-class dataset into multiple binary classification problems. A binary classifier is then trained on each binary classification problem and predictions are made using the model that's the foremost confidant.
• For instance, given a multi-class classification problem with examples for every class ‘red,’ ‘blue,’ and ‘green‘. this might be divided into three binary classification datasets as follows:
• Binary Classification Problem 1: red vs [blue, green] , Binary Classification Problem 2: blue vs [red, green] , Binary Classification Problem 3: green vs [red, blue] .
• A possible downside of this approach is that it requires one model to be created for every class. for instance, three classes require three models. this might be a problem for giant datasets, slow models, or very large numbers of classes (e.g. many classes).