Skip to content

Classification Accuracy

Definition
AI-generated

Classification accuracy is the fraction of predictions that match the observed class labels among all predictions (correct classifications divided by total).

Topics
Probability and inference
Synonyms
Accuracy
Related
Area Under the ROC Curve (AUC) Case-Control Study Calibration

Why it matters in GWAS

Binary case–control benchmarks for predictors—polygenic scores, biomarkers, or clinical models—sometimes report accuracy alongside AUC, sensitivity, and specificity; reviewers often prefer AUC, calibration, and absolute risk metrics because accuracy is threshold- and prevalence-sensitive.

Example usage

"The baseline risk model reached 72% classification accuracy at the default threshold, but logistic calibration was poor in South Asian participants."

Area Under the ROC Curve (AUC) Case-Control Study Calibration Polygenic Risk Score (PRS) Precision Recall Specificity
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

  • Steyerberg EW, et al. (2010). Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology.
← African Caribbeans in Barbados (ACB) Acetylation →

Last updated (UTC · Git history)