A simplified example of Multi-Label Classification is photo classification. This is where a given photo may have multiple objects in the scene and a model may predict the presence of multiple known objects in the photo, such as “bicycle,” “apple,” “person,” etc.

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Multi-label classification is a machine learning problem focused on predicting classes that are not mutually exclusive, allowing a single example to be assigned to multiple categories simultaneously. This formulation is particularly useful for auto-tagging tasks, where an item (such as a blog post or an image) might be associated with several correlated tags at once.

Multi-Label Classification

Developers need to know what works and how to use it. This site uses less math and more tutorials with working code.
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Example of Multi-Label Classification

Bernoulli distribution is a distribution over a single binary variable. It can only have results in 0 or 1. The sum of the probability of 0  and the probability of 1 is equal to 1. If we denote the probability of 0 as $\sigma$, Bernoulli distribution then has a mean of $\sigma$ and a variance of  $\sigma * (1- \sigma)$.

Bernoulli Distribution

•	Binary Relevance (first-order)
•	Classifier Chains (high-order)
•	Calibrated Label Ranking (second-order)

Problem Transformation Methods of Multi-Label Classification

Algorithm adaptation is a way to directly apply the existing single label algorithm to multiple labels, mainly including:

Multi-Label KNN：

For each instance, firstly, obtain the K instances closest to it, and then use the label set of these instances to judge the value of the predicted label set of this instance through the maximum a posteriori probability (map).

Multi-Label Decision Tree：

The core of using the decision tree to deal with multi-label content is to give more fine-grained information entropy gain criteria to build this decision tree model.

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