What is one-vs.-all?
A machine learning strategy used for multi-class classification problems
one-vs.-all explained in plain English
One-vs.-all is a technique where a single classifier is trained to distinguish one class from all the other classes combined, and this process is repeated for each class
Analogy
Imagine you're trying to identify different types of fruit. In one-vs.-all, you would first train a model to distinguish apples from all other fruits, then train another model to distinguish bananas from all other fruits, and so on
Example
A spam filter using one-vs.-all could train one model to identify spam emails versus non-spam emails, and another model to identify promotional emails versus non-promotional emails
How is one-vs.-all used?
This strategy is often used in machine learning algorithms such as support vector machines and logistic regression to solve multi-class classification problems
Common misconceptions about one-vs.-all
One common misconception is that one-vs.-all is the only approach to multi-class classification, when in fact there are other strategies such as one-vs.-one and all-vs.-all
History
The one-vs.-all strategy has been used in machine learning for decades, and is a well-established technique in the field
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