Multi-Class Classification
Multi-Class Classification is a supervised machine learning task where a model assigns an input to one of three or more mutually exclusive classes. Unlike binary classification, it requires the model to distinguish among multiple possible categories.
What is Multi-Class Classification?
In multi-class classification, the model learns from labeled examples and predicts one class for each input. For example, an image classifier might categorize an image as a cat, dog, bird, or horse. Models can use techniques such as softmax to produce probabilities for each possible class.
Why is Multi-Class Classification Important?
Multi-class classification enables AI systems to make decisions across multiple categories rather than limiting predictions to two outcomes. It is useful for automating categorization and identifying the most likely class among several possible choices.
Common use cases
Multi-class classification is commonly used for image recognition, document classification, sentiment analysis, language identification, medical diagnosis, fraud categorization, and customer segmentation.