Multi-class vs. Multi-label Classification: What’s the Difference?
Machine learning is a complex topic, and it can be challenging to understand all of the concepts involved. One of the most confusing aspects for beginners is the difference between multi-class and multi-label classification. This blog post will explain the difference between these two concepts and help you understand which one is right for your data.
Multi-class classification
There are only three animal species in our hypothetical world: a cat, a dog, or a chick. We have many pictures of animals, and we want to classify them into three different classes based on the animal.
If we can correctly identify the animal in each picture, we can put it into the correct category. For example, if a picture contains a cat, we would put it into the “cat” category. However, if a picture contains a dog, we would put it into the “dog” category. Similarly, if a picture contains a chick, we would put it into the “chick” category. By correctly categorizing the animals in each image, we can help to ensure that each animal is placed in the correct class.
As I just presented to you, this problem is a multi-class classification problem. We have samples (images), and each sample belongs to a class. We assume that a picture is either a cat, a dog, or a chick, but not a combination of them!
Multi-label classification
Let’s say we have a different problem now. We want to classify pictures of animals, but this time there can be more than one animal in each image!
For example, a picture might contain both a cat and a dog. We would put the image into the “cat” and the “dog” categories. Similarly, a picture might contain a cat, a dog, and a chick. In this case, we would put the image into the “cat” category, the “dog” category, and the “chick” category.
This problem is a multi-label classification problem. We have samples (images), and each sample can belong to multiple classes. We assume that a picture can contain any combination of animals!
Summary
So, what’s the difference between multi-class and multi-label classification? In multi-class classification, each sample belongs to one and only one class. In contrast, each sample can belong to multiple classes in multi-label classification.
Thanks for reading :)
