Multiclass Classification

Multiclass classification extends binary classification to problems with three or more possible categories. Instead of choosing between just two options (like spam/not spam), the model must select from multiple distinct classes, such as identifying whether an image contains a cat, dog, or bird, or categorizing customer support tickets into "billing," "technical," or "general inquiry".

The challenge grows with each additional class because the model must learn to distinguish not just "A versus B," but "A versus B versus C versus D" and so on. Two common strategies help tackle this: One-vs-All trains a separate binary classifier for each class against all others combined, while One-vs-One creates classifiers for every pair of classes. Modern algorithms like neural networks and decision trees can also handle multiple classes directly without breaking the problem down.

Multiclass classification appears everywhere in daily life: language detection (English, Spanish, French), product categorization, handwriting recognition (digits 0-9), and medical diagnosis with multiple possible conditions. Evaluating these models requires metrics that account for the added complexity; accuracy shows overall performance, while confusion matrices reveal which specific classes the model confuses most often.