THE RESULT
What the data shows
The largest computed metric is 2.35 for Median baseline; the smallest is 1.55 for Extra trees. Metric: mae (Ring-count MAE).
The target is ring count. Some predictors require dissection, so this full-feature model is not a non-destructive measurement protocol.
| model | mae | rmse | r2 | bias |
|---|---|---|---|---|
| Median baseline | 2.35 | 3.424 | -0.09594 | -1.013 |
| Ridge | 1.634 | 2.316 | 0.4985 | -0.008641 |
| Extra trees | 1.55 | 2.247 | 0.5283 | -0.04281 |
THE METHOD
From source to answer
Grouped-input regression with original ring-count errors.
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: rings
Inputs: sex, length, diameter, height, whole_weight, shucked_weight, viscera_weight, shell_weight
THE NEXT DECISION
What follows from the finding
Evaluate with independently collected animals and sampling sites before biological use.
Where the conclusion stops
Physical measurements use source scaling. Rings are the modeled target; the source describes an approximate age conversion. Site and animal group identifiers are unavailable, limiting biological generalization.
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.