Supervised vs. Unsupervised Learning
Executive Summary
Key Takeaways
- ✓ Supervised learning trains a machine learning model on historical data paired with a known, labelled outcome, well suited to tasks like forecasting and credit scoring where past outcomes are recorded.
- ✓ Unsupervised learning identifies structure or groupings in data without labelled outcomes, well suited to tasks like anomaly detection and segmentation where no predefined label exists for what counts as normal or abnormal.
- ✓ Choosing between the two depends on whether labelled historical outcomes exist for the task at hand, not on which approach is considered more advanced.
- ✓ Both approaches remain subject to the same empirical reliability standard, measured accuracy against held-out data, that applies to machine learning generally.
Definition¶
Supervised learning trains a machine learning model on historical data paired with a known, labelled outcome. Unsupervised learning identifies structure or groupings in data without labelled outcomes.
Choosing Between the Two¶
Supervised learning is well suited to tasks like forecasting or credit scoring, where past outcomes are recorded and available to train the model. Unsupervised learning is well suited to tasks like anomaly detection or segmentation, where no predefined label exists for what counts as normal or abnormal, and the model instead identifies structure in the data itself. The choice between the two depends on whether labelled historical outcomes actually exist for the task at hand, not on which approach is considered more advanced.
Reliability Standard¶
Both approaches remain subject to the same empirical reliability standard addressed in Machine Learning vs. Financial Modelling: measured accuracy against held-out data the model was not trained on.
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Frequently Asked Questions
What is supervised learning?
A machine learning training approach where a model learns from historical data paired with a known, labelled outcome, well suited to tasks like forecasting or credit scoring, where past outcomes are recorded and can be used to train the model.
What is unsupervised learning?
A machine learning training approach where a model identifies structure or groupings in data without labelled outcomes, well suited to tasks like anomaly detection or segmentation, where no predefined label exists for what counts as normal or abnormal.
How should a finance team choose between the two approaches?
Based on whether labelled historical outcomes exist for the task at hand, supervised learning where they do, unsupervised learning where they do not, rather than on which approach is considered more sophisticated.
Are supervised and unsupervised learning held to the same reliability standard?
Yes. Both remain subject to the same empirical reliability standard that applies to machine learning generally, measured accuracy against held-out data the model was not trained on.
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