AI BriefingThe Trap of LightGBM Feature Importance
Key point
The case analyzed a target encoding feature that showed high importance yet failed to generalize.
Details
In a price prediction engine using LightGBM quantile regression, a phenomenon occurred where prediction performance degraded despite high feature importance.
When a Variant-conditioned Bayesian target encoder was introduced, this feature recorded an overwhelming #1 feature importance based on q90 across all seed runs.
However, testing on the validation dataset showed that performance actually degraded, with MAPE (Mean Absolute Percentage Error) rising by 0.28pp.
The cause was that the encoder overfit by learning irreducible label variance—such as product condition and seller behavior—that cannot be captured as features. This case demonstrates that high feature importance does not guarantee a model's generalization performance.
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