Beginner · Fundamentals

Classification

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. A supervised learning task where the model assigns inputs to discrete categories.

Technical Definition

A supervised learning task where the model assigns inputs to discrete categories.

How it works

Classification predicts a label from a fixed set: spam vs not spam, image of digit 0–9, sentiment positive/negative/neutral. Binary classification has two classes; multi-class has many; multi-label allows multiple labels per input. Common algorithms: logistic regression, SVMs, neural networks. Evaluated with accuracy, precision, recall, and F1.

Related Concepts

• Supervised Learning — Learning from input–output pairs where each training example carries a correct label.
• Accuracy — The fraction of predictions a model gets correct on a labeled dataset.
• F1 Score — The harmonic mean of precision and recall, balancing both into a single number.