Comprehensive evaluation showing R² of 0.840, RMSE of £1,900, and stable cross-validation
All metrics computed on the held-out test set (33% of data)
£1,400 — On average, predictions are off by £1,400 from the actual price.
This is the most intuitive error metric. Lower is better.
£1,900 — Typical prediction error, with more penalty for large mistakes.
Squares errors before averaging, so large errors are weighted more heavily.
0.840 — The model explains 84% of the variance in price.
Range: 0 to 1. Higher is better. 0.80+ is considered excellent for regression.
0.839 — R² adjusted for the number of features in the model.
Penalises adding too many features. Very close to R², indicating good feature selection.
0.840 — Average R² across 5 different train/test splits.
Cross-validation tests how well the model generalises to unseen data.
< 0.01 — Very low variance across CV folds.
Low std means the model is stable and not sensitive to the data split.
A dealer using this model can expect predictions within approximately £1,900 of the true market price for a typical Ford vehicle. The model provides a consistent, objective baseline that removes human guesswork from initial appraisals. Hybrid and Electric vehicles should be reviewed manually as the model has limited training data for these categories.
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To ensure the model isn't overfitting to a particular train/test split, we performed 5-Fold Cross Validation. The dataset is split 5 times, and the model is trained and evaluated on each split.
| Fold | R-squared | Status |
|---|---|---|
| Fold 1 | ~0.840 | Excellent |
| Fold 2 | ~0.840 | Excellent |
| Fold 3 | ~0.841 | Excellent |
| Fold 4 | ~0.839 | Excellent |
| Fold 5 | ~0.840 | Excellent |
| Mean | ~0.840 | Very Stable |
| Std Deviation | < 0.01 | Excellent |
The extremely low standard deviation (< 0.01) across all 5 folds confirms the model generalises well and is not overfitting to any particular data split. This is a very strong indicator of model stability.
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