THE RESULT
What the data shows
The largest computed metric is 11.95 for Median baseline; the smallest is 3.798 for Extra trees. Metric: mae (MPG MAE).
The split is by ordered records and may split the boundary year; it does not imply exact release-date chronology.
| model | mae | rmse | r2 | bias |
|---|---|---|---|---|
| Median baseline | 11.95 | 13.31 | -3.94 | -11.89 |
| Ridge | 4.174 | 5.52 | 0.1501 | -2.948 |
| Extra trees | 3.798 | 5.178 | 0.2523 | -2.775 |
THE METHOD
From source to answer
Chronological-by-model-year evaluation; fixed baseline and regression models.
The Python source exposes this study’s transformations. The complete project download includes shared preparation and evaluation routines.
EVALUATION
How the result was checked
First 80% of ordered observations train; final 20% test.
Fixed configurations specified before scoring; no tuning on the holdout.
Target and input features
Target: mpg
Inputs: cylinders, displacement, horsepower, weight, acceleration, model_year, origin
THE NEXT DECISION
What follows from the finding
Compare temporal transfer with random grouped evaluation to understand extrapolation limits.
Where the conclusion stops
Historical city-cycle vehicle observations are not current fleet performance. Vehicle mix and model year are confounded. A '?' horsepower value means missing, not zero.
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.