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.

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.

Algorithm Adaptation

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.

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In machine learning, classification refers to a predictive modeling problem where a class label is predicted for a given example of input data. There are three main types of classification tasks that you may encounter; they are:
- Binary Classification,
- Multi-Class Classification,
- Multi-Label Classification.


3 Types of Classification Tasks in Machine Learning

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Multi-Label Classification

Multiclass-multioutput classification (also known as multitask classification) is a classification task which labels each sample with a set of non-binary properties. Both the number of properties and the number of classes per property is greater than 2. A single estimator thus handles several joint classification tasks. This is both a generalization of the multilabel classification task, which only considers binary attributes, as well as a generalization of the multiclass classification task, where only one property is considered.

https://scikit-learn.org/stable/modules/multiclass.html

Multiclass-multioutput Classification

Binary classification is the simplest form of classification problem where there are exactly two possible classes or categories to predict. A common example is designing a classifier that takes an image as input and outputs the predicted class assignment from a set of two options, such as $$\textrm{\{cat, dog\}}$$.

Binary Classification

Multiclass classification is a type of classification problem where the goal is to assign an example to one of more than two possible classes. A typical application is handwritten character recognition, which involves categorizing inputs into many distinct, mutually exclusive categories such as $$\textrm{\{0, 1, 2, ... 9, a, b, c, ...\}}$$.

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