Multi-Class Classification VS Multi-Label Classification

Author(s): Harshit Dawar

Originally published on Towards AI.

This blog aims to clearly distinguish the two most simultaneously used terminologies, yet very different from each other: “Multi-Label Classification” & “Multi-Class Classification!”

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Let’s clear the confusion between Multi-Class Classification & Multi-Label Classification once & for all.

Many people use these 2 terms simultaneously or as synonyms because they seem to be similar, but they are very different from each other & are used in separate contexts. Each term is used to solve a specific problem and has its own significance.

Both in Machine Learning & Deep Learning, there exist many problems, where the use of both of these is evident. It’s very crucial to understand the difference between the two in order to appropriately construct the approach to fulfill the goal.

This article will act as a one-stop-shop solution to clear out the confusion & act accurately.

Let’s demystify them.

To properly distinguish between the two, the distinction is made based on several parameters mentioned below:

Multi-Class classification is used when a record has to be classified into exactly 1 category/class, whereas Multi-Label classification is used when a record has to be classified into more than 1 category/class simultaneously.

Multi-Class Classification Example ♦️

An image is classified as a “Tiger” category.A document is classified as a “confidential” category.

Multi-Label Classification Example ☀️

An image is… Read the full blog for free on Medium.

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Published via Towards AI