Definition
Supervised Learning is a Machine Learning method that trains a model using examples containing known target values or labels.
Human Explanation
The model learns from examples for which the expected output is already known.
Why it Matters
It is widely used to build classification and regression models for outcomes that can be represented by labelled training examples.
Conceptual Boundary
Supervised Learning requires suitable labelled data. Labels may be incomplete, inconsistent or biased, and the model learns relationships represented in the training data rather than universal rules.
Practical Perspective
Applications include document classification, fraud detection and value prediction. Performance must be evaluated on data not used to train the model and under conditions representative of intended use.