Definition
Classification assigns each input to one of a set of classes. Binary classification has two labels; multiclass has many; multilabel allows several labels at once.
Metrics include accuracy, precision, recall, and F1, especially important when classes are imbalanced.
In simple terms
Classification is sorting fruit into bins labeled apple, orange, or banana, each item gets a category tag.
Where you see it
- Language identification for Somali vs English text.
- Medical image disease vs healthy.
- Intent classification in chatbots.
How it works
1.Label examples
Each training item gets a class.
2.Train a model
Learn decision boundaries or probabilities per class.
3.Predict
Output the most likely class (or a probability distribution).
Why it matters
- Classification is one of the most common production ML tasks across products and research.
Often confused
High accuracy always means a good classifier.
On imbalanced data, a model that always predicts the majority class can score high accuracy while being useless.