Model Evaluation
Cross-source consensus on Model Evaluation from 1 sources and 6 claims.
1 sources · 6 claims
Benefits
Preparation
Comparisons
Evidence quality
Highlighted claims
- The main test evaluation uses 20% of total samples as a dedicated test set, with the remaining 80% split into training and validation. — Learning Multi-Relational Graph Representations for DNA Methylation-Based Biological Age Estimation
- The study uses MAE, MSE, and fitting regression coefficient as auxiliary metrics for predictive consistency. — Learning Multi-Relational Graph Representations for DNA Methylation-Based Biological Age Estimation
- The full three-graph RelAge-GNN model had the lowest MSE and stable prediction behavior in the ablation study. — Learning Multi-Relational Graph Representations for DNA Methylation-Based Biological Age Estimation
- Removing G1 or G3 caused the largest error increases. — Learning Multi-Relational Graph Representations for DNA Methylation-Based Biological Age Estimation
- The reported RelAge-GNN test value for fitting regression coefficient was 0.962. — Learning Multi-Relational Graph Representations for DNA Methylation-Based Biological Age Estimation
- The reported RelAge-GNN test MAE was about 3.4 to 3.43 and MSE was 29.46. — Learning Multi-Relational Graph Representations for DNA Methylation-Based Biological Age Estimation