Four Training Models: Supervised, Unsupervised, Semi-supervised, Reinforcement Learning
Machine Learning
Classification
AI/ML Fundamentals
Overview
The four primary training models in machine learning are supervised, unsupervised, semi-supervised, and reinforcement learning. Supervised learning uses labeled data to train algorithms to classify data or predict outcomes. Unsupervised learning identifies patterns or groupings in unlabeled data, often used for clustering or dimensionality reduction. Semi-supervised learning bridges these two, leveraging a small amount of labeled data with a large amount of unlabeled data to improve learning accuracy. Reinforcement learning involves agents learning optimal actions through trial and error, guided by rewards or penalties. These paradigms are foundational but not mutually exclusive-hybrid models and transfer learning can blur boundaries. A limitation is that real-world data often does not fit cleanly into one paradigm, requiring nuanced model selection and sometimes custom approaches. Additionally, the success of each approach depends on data quality, availability of labels, and the specific problem context.
Governance Context
From a governance perspective, organizations must ensure data labeling practices (supervised/semi-supervised) comply with privacy frameworks like GDPR, which mandates lawful processing and data minimization. Reinforcement learning systems, especially in dynamic environments, may require ongoing monitoring under ISO/IEC 23894:2023 to detect emergent harmful behaviors. Controls such as NIST AI RMF's 'Data and Input Validity' and 'Performance Monitoring' are essential for all four models. Additionally, documentation obligations under the EU AI Act require clear articulation of model selection rationale and associated risks, particularly where unsupervised or reinforcement learning models are deployed in high-stakes contexts. Two concrete obligations include: (1) Implementing robust data provenance and labeling documentation to demonstrate compliance with data protection laws, and (2) Establishing continuous performance monitoring and incident reporting mechanisms to promptly identify and mitigate unintended model behaviors.
Ethical & Societal Implications
The choice of training model impacts fairness, accountability, and transparency. Supervised and semi-supervised learning may inherit biases from labeled data, while unsupervised models can reinforce hidden structures that disadvantage minority groups. Reinforcement learning systems may develop unsafe or unethical strategies if reward functions are poorly designed or misaligned with societal norms. These issues necessitate robust oversight, continuous monitoring, and stakeholder engagement to ensure systems do not perpetuate harm or undermine trust. Additionally, semi-supervised models raise concerns about the use of large amounts of unlabeled data, potentially implicating consent and data provenance issues.
Key Takeaways
Four main training models underpin most machine learning systems.; Model choice affects explainability, risk, and governance obligations.; Supervised and semi-supervised models require careful data labeling and privacy controls.; Reinforcement learning needs ongoing monitoring to detect emergent risks.; Real-world applications often require hybrid or nuanced approaches beyond textbook definitions.; Regulatory compliance requires documentation of model choice and risk management.; Bias and fairness concerns are present across all training models and must be addressed.