- Face classification
- Plant species classification
- Optical character recognition

University of Michigan - Ann Arbor

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 $$\textrm{\{0, 1, 2, ... 9, a, b, c, ...\}}$$.

Multiclass 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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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 


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