Regulariseret LightGBM
Regulariseret LightGBM anvender L1 (lasso) og L2 (ridge) straf-termer på blad-vægt-objektivet i LightGBM — Microsofts yderst effektive gradient boosting-framework — for at kontrollere modelkompleksitet, reducere overfitting og forbedre generalisering på tabelklassifikations- og regressionsopgaver med højdimensionelle eller støjfyldte featuresæt.
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Method map
The neighbourhood of related methods — select a node to explore.
Kilder
- 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. link ↗
- Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. DOI: 10.1145/2939672.2939785 ↗
Sådan citerer du denne side
ScholarGate. (2026, June 3). Regularized Light Gradient Boosting Machine. ScholarGate. https://scholargate.app/da/machine-learning/regularized-lightgbm
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- CatBoostMaskinlæring↔ compare
- Gradient BoostingMaskinlæring↔ compare
- LightGBMMaskinlæring↔ compare
- Regulariseret gradient-boostingMaskinlæring↔ compare
- XGBoostMaskinlæring↔ compare
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