One-vs.-all

See also: Machine learning terms

One-vs.-All in Machine Learning

One-vs.-all (OvA), also known as one-vs.-rest (OvR) or one-against-all, is a multi-class classification strategy commonly used in machine learning. It is a method for training a classifier to distinguish between multiple classes by converting the multi-class problem into several binary classification problems. The key idea is to train a separate binary classifier for each class, treating it as the positive class and combining all other classes as the negative class.

Approach

The one-vs.-all approach involves the following steps:

For each class in the dataset, create a separate binary classification problem, treating the current class as the positive class and all other classes as the negative class.
Train a binary classifier on each of these binary problems, resulting in a set of classifiers.
To classify an unseen instance, input the instance to each of the classifiers and obtain their predictions.
Determine the final class label by selecting the class with the highest confidence score or probability among the classifiers.

The one-vs.-all strategy is often used with a variety of machine learning algorithms, such as support vector machines, logistic regression, and neural networks. It is particularly useful when the number of classes is large or when the underlying classifier is inherently binary.

Advantages and Disadvantages

The one-vs.-all approach has some advantages and disadvantages when compared to other multi-class classification techniques, such as one-vs.-one or error-correcting output codes.

Advantages:

OvA is computationally efficient, as it requires training only one classifier per class.
It can easily be parallelized, as the classifiers can be trained independently of one another.
OvA can be employed with any binary classification algorithm, making it versatile and adaptable.

Disadvantages:

The method may suffer from class imbalance problems, as the number of negative examples often significantly exceeds the number of positive examples for each binary classification problem.
OvA can be less accurate than other multi-class strategies, particularly when the classes are not linearly separable.
The approach may be less interpretable, as the classifiers do not directly provide information about pairwise relationships between classes.

Explain Like I'm 5 (ELI5)

Imagine you have a basket of different fruits (apples, bananas, and oranges) and you want to teach a robot how to recognize each fruit. The one-vs.-all method works by first teaching the robot how to recognize apples from all other fruits, then bananas from all other fruits, and finally oranges from all other fruits. The robot learns to identify each fruit separately, but when it comes to recognizing a new fruit, it will use all of its knowledge to decide which fruit it is. If the robot is more confident that the new fruit is an apple than a banana or an orange, it will say the fruit is an apple.