- Face classification
- Plant species classification
- Optical character recognition

Examples of Multi-Class Classification

- Macro-average: each class has equal weight. 
1. Compute metric with each class 
2. Average resulting metrics across classes 
- Micro-average: Each instance has equal weight, largest class has most influence
1. Aggregate outcomes across classes
2. Compute metric with aggregate outcomes 

If want to weight metric towards largest classes → use micro-averaging
If you want to weight metric towards smallest classes → use macro-averaging 


Multi-Class Evaluation Metrics

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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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 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

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 $$\textrm{\{cat, dog\}}$$.

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