Explain the theories behind the AI/ML solutions you develop for a case study or take-home challenge, and how you choose one to elaborate. Using the supplied PostgreSQL tables, write a query that identifies the most defensible solution for each case based on documented theory, reproducibility, and improvement from an earlier evaluation. Exclude solutions without sufficient theory documentation or a measurable improvement.
case_name, solution_name, approach_family, theory_count, previous_auc, latest_auc, auc_improvement, and evidence_score.case_name, then highest evidence_score, with solution_id as the final tie-breaker.| Column | Type | Description |
|---|---|---|
| case_idPK | INT | Case study identifier |
| case_name | VARCHAR(100) | Case study name |
| Column | Type | Description |
|---|---|---|
| solution_idPK | INT | Solution identifier |
| case_id | INT | Referenced case study |
| solution_name | VARCHAR(120) | Name of the AI or ML solution |
| approach_family | VARCHAR(60) | Broad modeling approach |
| reproducibility_score | DECIMAL(4,3) | Reproducibility score from 0 to 1 |
| Column | Type | Description |
|---|---|---|
| run_idPK | INT | Evaluation run identifier |
| solution_id | INT | Referenced solution |
| evaluated_at | DATE | Evaluation date |
| validation_auc | DECIMAL(5,4) | Validation AUC |
| status | VARCHAR(20) | Run status |
| Column | Type | Description |
|---|---|---|
| note_idPK | INT | Theory note identifier |
| solution_id | INT | Referenced solution |
| theory_name | VARCHAR(100) | Theory or method documented |
| evidence_level | VARCHAR(20) | Strength of supporting evidence |