Why our #1 LightGBM feature by importance made predictions worse [D]

In three linesA LightGBM quantile regression model ranked a Bayesian target-encoded feature #1 by importance for watch price forecasting, but 4-seed × 3-variant ablation showed +0.28pp MAPE regression on hold-out. The learned signal was irreducible label noise (unobserved factors), failing to generalize.

Summary generated by Claude — human-verified