Skip to content

Mean Absolute Error (MAE)

Definition
AI-generated

The mean absolute error (MAE) averages absolute differences between predicted and observed values: (1/n) Σ |yᵢ − ŷᵢ|.

Topics
Machine learning concepts
Synonyms
L1 loss (when used as a training objective)
Related
Coefficient of Determination (R²) Machine Learning Mean Absolute Percentage Error (MAPE)

Why it matters in GWAS

MAE summarizes error for polygenic prediction of quantitative traits (e.g. BMI, lipids), imputation quality panels, or embedding regression; compare against the scale and naive baselines (mean predictor) when judging gains from genetic predictors.

Example usage

"The linear PRS reduced MAE for HDL by 0.08 mmol/L versus age and sex alone in the external cohort."

Coefficient of Determination (R²) Machine Learning Mean Absolute Percentage Error (MAPE) Mean Squared Error (MSE) Polygenic Risk Score (PRS) Root Mean Squared Error (RMSE)
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

  • Willmott CJ, Matsuura K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE). Clim Res.
← Maximum likelihood estimation (MLE) Mean Absolute Percentage Error (MAPE) →

Last updated (UTC · Git history)