What is classification in machine learning?

Classification in machine learning is a supervised learning task in which a model predicts which category (class) an observation belongs to.

Unlike regression, which predicts numerical quantities, classification predicts discrete labels such as spam or not spam.

Examples

• Email filtering: Spam or Not Spam.
• Loan risk: Default or No Default.
• Document sorting: Finance, Sports, or Technology.

How does classification work?

1. Training: The model learns patterns from examples containing input features and their known class labels.
2. Prediction: For a new observation, it uses those patterns to predict a class. Many classifiers can also output a probability or score for each class.

Example: A spam classifier learns from previously labeled emails. For a new email, it may estimate a 90% probability of spam and classify it as Spam using a chosen decision threshold.

Main forms of classification

• Binary classification: choose between two classes, such as Yes and No.
• Multiclass classification: choose one class from three or more possibilities.
• Multilabel classification: assign multiple applicable labels to the same observation.

Classification vs regression

• Classification: predicts a category, even if that category is encoded as a number like 0 or 1.
• Regression: predicts a numerical quantity, such as price, temperature, or revenue.

How is a classifier evaluated?

Common metrics include accuracy, precision, recall, and F1-score. The appropriate metric depends on which classification mistakes matter most.

In short, classification teaches a model to assign observations to predefined categories using patterns learned from labeled training data.