Final Best model with benchmark and error breakdown

Build best final model from library path, and compare it's performance against the current best version.
Your report must include a side-by-side benchmark table (RMSE, R², MAE, MSE, and training runtime) and a stratified error breakdown showing the Mean Absolute Error (MAE) for the bottom, middle, and top 33% of target values to identify where the linear model's assumptions fail.

Start by locating the library path and the current best version referenced in the issue, then inspect the notebook workflow used to train and evaluate the linear model. Done means producing a final model comparison with RMSE, R², MAE, MSE, and training runtime, plus MAE for the bottom, middle, and top target-value thirds.

Written by the indexing model from the issue text.