THE RESULT
What the data shows
The largest computed metric is 0.4488 for white / 6; the smallest is 0.001021 for white / 9. Metric: within_type_share (Within-type sample share).
The table reports observed score support; absent ratings do not imply impossible products.
| wine type | quality | samples | within type share | label |
|---|---|---|---|---|
| red | 3 | 10 | 0.006254 | red / 3 |
| red | 4 | 53 | 0.03315 | red / 4 |
| red | 5 | 681 | 0.4259 | red / 5 |
| red | 6 | 638 | 0.399 | red / 6 |
| red | 7 | 199 | 0.1245 | red / 7 |
| red | 8 | 18 | 0.01126 | red / 8 |
| white | 3 | 20 | 0.004083 | white / 3 |
| white | 4 | 163 | 0.03328 | white / 4 |
| white | 5 | 1,457 | 0.2975 | white / 5 |
| white | 6 | 2,198 | 0.4488 | white / 6 |
| white | 7 | 880 | 0.1797 | white / 7 |
| white | 8 | 175 | 0.03573 | white / 8 |
THE METHOD
From source to answer
Counts and proportions by wine type and observed quality score.
The Python source exposes this study’s transformations. The complete project download includes shared preparation and evaluation routines.
THE NEXT DECISION
What follows from the finding
Use label prevalence to choose evaluation metrics before training a quality model.
Where the conclusion stops
Sensory scores are ordinal and concentrated in the middle. Producer and batch IDs are unavailable. Associations are not recipes for changing quality or evidence of market price.
Related studies may reuse observations or holdouts. These are historical analyses; associations and backtests do not demonstrate commercial impact. Further model tuning needs new, untouched evaluation data.