A side-by-side comparison of Classification and Regression. Understand how predicting discrete categories differs from predicting continuous or numerical values.
Veredicto rápido: Use Classification when the target is a category; use Regression when the target is a continuous or numerical value.
Classification describes supervised machine learning task in which a model assigns inputs to discrete categories.
Contexto: Most relevant when the desired output is a label, class, category, or decision group.
Regression describes supervised machine learning task in which the target output is a continuous or numerical value.
Contexto: Most relevant when the desired output is a quantity, estimate, score, or forecast.
| Aspecto | Classification | Regression |
|---|---|---|
| Definition | Classification assigns inputs to discrete categories. | Regression predicts a continuous or numerical target value. |
| Practical difference | The output is a class label, such as positive or negative, category A or category B. | The output is a number, such as a price, demand estimate, risk score, or temperature. |
| Typical use case | Used for spam detection, document category assignment, eligibility classes, and image labels. | Used for forecasting, valuation, demand prediction, and numerical estimation. |
| Common mistake | Forcing a numeric prediction problem into categories too early. | Treating a category decision as a regression task without a meaningful numeric target. |
| Governance implication | Requires class definitions, label quality checks, confusion matrix analysis, and threshold governance. | Requires target definition, error distribution review, calibration, and monitoring of prediction accuracy. |
In practice, the fastest way to choose is to ask what the output means. A label points to classification; a number points to regression.
Calling every predictive model a classifier.
Using accuracy for regression tasks.
Ignoring class imbalance in classification.
Turning continuous outcomes into categories without documenting the threshold.
Use Classification when the model output should assign an input to one or more discrete categories. It is the right term for binary, multiclass, and text classification tasks.
Use Regression when the model output should be a continuous or numerical value. It is the right term for prediction problems involving quantities, estimates, or real-valued targets.
Clear task type matters for validation evidence, performance claims, and risk assessment. AI governance documentation should specify the target, output type, evaluation metrics, and limitations rather than using generic model-performance language.
A raw numerical risk score is usually closer to regression. If the score is converted into categories such as low, medium, or high, the final decision layer becomes classification-like.
Yes. A system may predict a numerical score and then classify it into a category using thresholds or business rules.
Classification and regression use different metrics and error analysis. Mixing them can produce misleading validation evidence.
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