Deep Learning · TEE · section

R25 Q1b · Training and validation errors

February 2025 re-exam · complete solved paper

Question

Question. Choose best and worst models from training/validation errors: M1=0.9/1.2; M2=0.3/0.6; M3=1.5/0.5; M4=1.2/1.1. Explain the reasoning and remedies. (5 marks)

Answer — step by step

Model	Train	Validation
1	0.9	1.2
2	0.3	0.6
3	1.5	0.5
4	1.2	1.1

For the same validation set and same loss definition, M3 is best (0.5) and M1 is worst (1.2). Choose by validation performance, not the smallest training loss. M2 has the smallest training error but a larger validation error than M3.

M3’s validation loss below training loss is unusual but not logically impossible: training-only dropout/augmentation, different regularization inclusion or an easier validation sample can cause this. First verify comparable evaluation conditions and leakage-free splits. Error values alone do not prove overfitting or underfitting without baselines and learning curves.

For poor generalization, inspect data quality/splits and learning curves; if overfitting is established, use regularization, dropout, more suitable data or early stopping; if underfitting is established, improve model capacity/features or training. Do not prescribe a larger network solely from these four numbers.