Robust LightGBM
Robust LightGBM is a gradient boosting framework that pairs Microsoft's highly efficient LightGBM engine with outlier-resistant loss functions — most commonly Huber, quantile, or mean absolute error — so that predictions are not unduly distorted by extreme or erroneous observations. It retains LightGBM's speed and leaf-wise tree growth while providing resistance to heavy-tailed noise in the target variable.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
- Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems, 30, 3146–3154. · URL
- Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics, 29(5), 1189–1232. · DOI 10.1214/aos/1013203451
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