Supervised Learning
Definition
Supervised learning trains models with examples and known target values.
Machine learning of a mapping from inputs to target values based on labeled training examples.
Simply explained
The model receives practice tasks along with correct answers. From this, it learns to predict suitable answers for new tasks.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
A company trains a defect classification on professionally verified and separately evaluated images.
Limits and pitfalls
- Label quality, data leakage, distribution shifts, and inappropriate target metrics produce seemingly good models.
Origin & history
Supervised learning evolved from statistical classification and regression and was extended with neural networks.
Use in mid-sized companies
- Possible application: A company trains a defect classification on professionally verified and separately evaluated images.
Frequently asked questions
What does supervised learning mean?
Machine learning of a mapping from inputs to target values based on labeled training examples. What should be noted? Label quality, data leakage, distribution shifts, and inappropriate target metrics produce seemingly good models.
Sources
- Supervised learning · scikit-learn, Supervised learning
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: