THE RESULT
What the data shows
The largest computed metric is 6.101 for Median baseline; the smallest is 1.885 for Extra trees. Metric: mae (MPG MAE).
Car names and row identifiers are excluded. Horsepower imputation is fitted only on training observations.
| model | mae | rmse | r2 | bias |
|---|---|---|---|---|
| Median baseline | 6.101 | 7.275 | -0.002077 | -0.3312 |
| Ridge | 2.496 | 3.492 | 0.7692 | 0.2085 |
| Extra trees | 1.885 | 2.805 | 0.851 | 0.007504 |
THE METHOD
From source to answer
Fixed grouped-input regression with median and linear baselines.
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: mpg
Inputs: cylinders, displacement, horsepower, weight, acceleration, model_year, origin
THE NEXT DECISION
What follows from the finding
Validate on new vehicle designs and test conditions before making engineering predictions.
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.