Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/298578 
Autor:innen: 
Erscheinungsjahr: 
2024
Schriftenreihe/Nr.: 
Working Paper No. 2/2024
Verlag: 
Örebro University School of Business, Örebro
Zusammenfassung: 
This article presents a comprehensive study on developing a predictive product pricing model using LightGBM, a machine learning method optimized for regression challenges in situations with limited historical data. It begins by detailing the core principles of LightGBM, including decision trees, boosting, and gradient descent, and then delves into the method's unique features like Gradient-based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB). The model's efficacy is demonstrated through a comparative analysis with XGBoost, highlighting LightGBM's enhanced efficiency and slight improvement in prediction accuracy. This research offers valuable insights into the application of LightGBM in developing fast and accurate product pricing models, crucial for businesses in the rapidly evolving data landscape.
Schlagwörter: 
GBM
GBDT
LightGBM
GOSS
EFB
predictive model
JEL: 
E37
Dokumentart: 
Working Paper

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