Industrial AI
Supervised vs unsupervised learning in factories
Compare supervised and unsupervised learning using inspection data. See how labels, outputs and validation change the task you can support.
Supervised learning uses examples with a known target, such as a verified defect category or a measured value. Unsupervised learning looks for structure in data without those target labels, such as groups of similar observations.
Both need human decisions about the data and the intended use. An automatically discovered group still needs an explanation before an operating team can act on it.
IBM's comparison identifies the presence of target labels as the central distinction. In a factory, that distinction becomes a practical question about the records you can supply.
Start with the inspection records
The visual inspection case contains 10,000 fictional parts with independent checks establishing 100 defective and 9,900 good parts. Those verified outcomes support evaluating a classification task.
If equivalent records were used for supervised training, each training image would need the correct target for the task. A whole-part accept/reject label would not automatically provide the location or type of a defect.
An unsupervised method could group images by similarity without receiving those defect labels. The groups might reflect surface finish, orientation or lighting. Calling one group defective would require additional evidence.
| Question | Supervised task | Unsupervised task |
|---|---|---|
| What does training include? | Inputs and target labels | Inputs without that target label |
| What might the output describe? | A predicted class or numeric value | A grouping, representation or unusual pattern |
| What must be checked? | Performance on suitable unseen labelled records | Whether the structure is stable, interpretable and useful for the intended decision |
Check the target before choosing the model
A label can record a person's decision, a later laboratory result or an equipment event. Those are different sources of evidence. Keep the definition and timing with the record.
A disagreement between inspectors affects the target the model learns. The attribute agreement guide explains how repeatability, agreement between people and agreement with a reference differ.
Do not silently turn uncertain labels into good parts. Keep the uncertainty visible so the team can decide whether to review, exclude or separately model those cases.
Keep the test separate from development
Supervised models need test records that represent later use and were not involved in model choice. Unsupervised methods also need evaluation: finding clusters in one dataset does not establish that those clusters remain useful on the next production run.
The data-leakage guide explains why records from the same part, run or future period can make evaluation misleading.
Use the output the task supports
Regression that predicts a numeric target is usually supervised learning. A method that groups observations without target labels is unsupervised. Neither description tells you that the output is accurate enough for a production decision.
Before requesting more data or a different algorithm, write down the target you need, where its reference values come from and how the result will be checked on new work. That will tell you more about the feasible approach than a list of popular model names.