THE RESULT
What the data shows
The largest computed metric is 0.6421 for Median baseline; the smallest is 0.5199 for Extra trees. Metric: mae (Sensory-score MAE).
Identical input signatures stay in one split. Scores are ordinal, while this benchmark treats score differences numerically.
| model | mae | rmse | r2 | bias |
|---|---|---|---|---|
| Median baseline | 0.6421 | 0.8921 | -0.04212 | 0.1793 |
| Ridge | 0.5622 | 0.7325 | 0.2974 | -0.01169 |
| Extra trees | 0.5199 | 0.6718 | 0.409 | -0.00178 |
THE METHOD
From source to answer
Fixed grouped-input regression with original score MAE and RMSE.
The Python source exposes this study’s transformations. The complete project download includes shared preparation and evaluation routines.
EVALUATION
How the result was checked
Fixed 80/20 split of unique input groups (seed 42); exact input duplicates stay together.
Fixed configurations specified before scoring; no tuning on the holdout.
Target and input features
Target: quality
Inputs: fixed_acidity, volatile_acidity, citric_acid, residual_sugar, chlorides, free_sulfur_dioxide, total_sulfur_dioxide, density, ph, sulphates, alcohol, wine_type
THE NEXT DECISION
What follows from the finding
Use error magnitude to judge whether laboratory features support an additional quality-screening study.
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.