Interpreting validation results involves understanding what the metrics signify and how they relate to the model's performance:
1. High Sensitivity and Specificity: Indicates that the model can accurately identify true positives and true negatives. 2. Overfitting Indicators: If the model performs exceptionally well on training data but poorly on validation data, it may be overfitting. 3. Generalizability: Good performance across multiple datasets and settings suggests that the model is generalizable and robust.
