Validation Matrix

Project phase: Model Evaluation and Validation

You build a structured overview comparing your candidate models against the criteria that matter for this project: each row a model or configuration, each column a criterion, combining performance metrics with quality aspects such as robustness, fairness across subgroups, interpretability, and training or inference cost. All results come from the same validation setup and held-out data so the comparison is fair, and you close with a substantiation that weighs the trade-offs and names the model you advise, including why the runner-up was rejected.

Starting Points

Validation Matrix

Key Points

I have clearly described the validation setup and the held-out data used for all models to ensure a fair comparison.
The matrix includes both technical performance metrics (e.g., accuracy, F1-score) and quality aspects (e.g., robustness, fairness, interpretability, or cost).
I have analyzed the results from multiple perspectives, including societal and ethical consequences (e.g., bias or transparency).
My advice is a logical conclusion of the matrix data, not just a personal preference.
I have explicitly justified why the runner-up model was not chosen.
The deliverable is a standalone markdown document, made available in GitLab.