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Mean Absolute Percentage Error (MAPE)

Definition
AI-generated

The mean absolute percentage error (MAPE) averages absolute percentage residuals, often (100/n) Σ |yᵢ − ŷᵢ| / |yᵢ|, sometimes with protection when yᵢ is near zero.

Topics
Machine learning concepts
Related
Mean Absolute Error (MAE) Mean Squared Error (MSE) Polygenic Risk Score (PRS)

Why it matters in GWAS

MAPE appears in some polygenic prediction and imputation benchmarks; when traits or read counts can be zero or very small, reported MAPE depends on trimming or weights, so MAE or RMSE in natural units may be clearer for genetic prediction papers.

Example usage

"The continuous trait team reported MAPE only for participants above the 5th percentile of observed BMI to avoid division blow-ups."

Mean Absolute Error (MAE) Mean Squared Error (MSE) Polygenic Risk Score (PRS)
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

  • Hyndman RJ, Koehler AB. (2006). Another look at measures of forecast accuracy. Int J Forecast.
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