36RanRegression

Car price

I kept 14 numeric columns, trained a decision tree regressor on 80% of 205 cars and reported a score of 1.0.

What it printed in 2022

Numbers read from the saved notebook outputs. Cell numbers count every cell in the notebook, Markdown included, starting at 1.
Output	Value	Cell #
Reported R² (scored against its own predictions)	1.000	19

Cell # counts every cell from the top of the notebook, Markdown included, starting at 1.

The score compared the predictions with themselves (model.score(X_test, predictions)), which returns 1.0 for any model.

A 205-row car table, a cleaned re-upload of the 1985 Automobile data from the UCI repository.

36
Conclusion flips
The perfect score that compared predictions with themselves
The 2022 call scored the predictions against themselves, which returns 1.0 for any model. Scored against the real prices, the same tree reaches R² 0.85, with a typical miss of $2,372.
Read the audit