Classification

Classification is assigning an input to one or more defined categories, often with a score or probability for each.

Plan for this page

Understand one concept in plain language and connect it to nearby ideas.

You will leave with: A classifier learns or applies a boundary between categories such as spam/not spam or several species. Designers must define the classes, examples, thresholds, and treatment of uncertain or unseen cases.
Time: 3–8 min
Before you begin: No prerequisite. Keep one real example in mind.
Do this now: Read the explanation, then compare the example with the common misunderstanding.

Prepared next stepStudy pattern and bias scenarios

ClassificationClassification is assigning an input to one or more defined categories, often with a score or probability for each.

Label
Prediction
Evaluation

Plain explanation

A classifier learns or applies a boundary between categories such as spam/not spam or several species. Designers must define the classes, examples, thresholds, and treatment of uncertain or unseen cases.

Example

An image system can assign a road sign to stop, yield, speed limit, or unknown and report confidence for each option.

Why it matters

Classifications often trigger decisions, so error costs and category design matter as much as overall accuracy. Different thresholds trade false positives against false negatives.

Common misunderstanding

Misconception: classification reveals the one natural category an item truly has. Correction: categories are defined for a purpose and may be incomplete, contested, or overlapping.

Related concepts

Follow relationships, not only the alphabet

LabelA label is a target value or category attached to an example for training or evaluating a supervised model.PredictionA prediction is a model’s estimated output for a given input, including a class, value, probability, sequence, or next element.EvaluationEvaluation is the structured measurement and investigation of how a model or system performs against defined questions, criteria, and contexts.

Study pattern and bias scenarios

Sources and scope

What supports this object

Machine Learning GlossaryGoogle for Developers

Defines classification tasks and metrics; real categories may overlap, change, or reflect policy choices.

Open source

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