Make the comparison easier to trust
A useful experiment record connects a change to a question. Write the question before the training run and keep the result beside the exact settings used.
Record the essentials
- Question: What specific uncertainty will this experiment resolve?
- Data: Dataset version, split procedure, and preprocessing.
- Model: Architecture, pretrained weights, and trainable layers.
- Training: Seed, optimizer, learning rate, batch size, and epochs.
- Evaluation: Data partition, metrics, threshold, and label order.
- Result: Numbers, representative mistakes, and relevant variability.
- Interpretation: What the comparison supports and what it leaves open.
- Next action: One justified follow-up or a decision to stop.
Keep the final test separate
Use validation data to choose settings. Evaluate the selected procedure on the held-out test data. Repeatedly adjusting a model after inspecting test results weakens the meaning of a final test.
The downloadable template includes a simple comparison table you can keep with your code.
A standalone resource by Ackley Will. About the author · Share a question or observation