# R25 Q1b · Training and validation errors

Subject: Deep Learning

Exam: TEE

Reader: https://deep-learning-study-room.vercel.app/#tee/read/21/tee-c21-s1

Canonical: https://deep-learning-study-room.vercel.app/read/dl/tee/section/tee-c21-s1/

## 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.