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Mean Squared Error (MSE)

Definition
AI-generated

The mean squared error (MSE) averages squared residuals: (1/n) Σ (yᵢ − ŷᵢ)².

Topics
Machine learning concepts
Synonyms
L2 loss (when used as a training objective)
Related
Coefficient of Determination (R²) Machine Learning Mean Absolute Error (MAE)

Why it matters in GWAS

MSE is a standard metric for continuous trait prediction, dosage or imputation error, and residual variance in polygenic models; compare against null models and report scale so improvements are interpretable (e.g. same units as the phenotype).

Example usage

"The CNN baseline lowered MSE for predicted expression compared with elastic net in held-out GTEx tissues."

Coefficient of Determination (R²) Machine Learning Mean Absolute Error (MAE) 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

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