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Supervised Learning

Definition
AI-generated

Supervised learning trains models from input–output pairs by minimizing prediction error on labeled data; common objectives include cross-entropy for classification and squared or robust losses for regression, with generalization estimated by held-out or cross-validated performance.

Topics
LLM and Agents
Related
Area Under the ROC Curve (AUC) Machine Learning Polygenic Risk Score (PRS)

Why it matters in GWAS

Case–control GWAS itself fits supervised models (often linear or mixed) at each variant; PRS training, phenotype imputation from EHR, and variant pathogenicity classifiers are also supervised. Label leakage (related samples, population stratification) can inflate accuracy unless study design and splits address genetics explicitly.

Example usage

"We treated three-year T2D incidence as the supervised learning target and used elastic net and gradient boosting baselines."

Area Under the ROC Curve (AUC) Machine Learning Polygenic Risk Score (PRS) Transfer Learning Unsupervised Learning
Related-term network

Click any node to open that term page.

Node order. The current term is first. Next come every page linked from Related terms, in the same top-to-bottom order as that list. Then new terms appear in two-hop layers: for each of those direct neighbors (in that same order), their related terms are appended if not already listed. A third pass does the same for two-hop nodes. The walk stops when a fixed maximum number of nodes is reached.

References

  • Hastie T, Tibshirani R, Friedman J. (2009). The Elements of Statistical Learning. Springer.
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