The box office prediction model based on the optimized XGBoost algorithm in the context of film marketing and distribution.

ABSTRACT: To improve the accuracy and efficiency of box office prediction, this study deeply discusses the application of the optimized eXtreme Gradient Boosting (XGBoost) model in this scenario and its advantages compared with other commonly used machine learning models. By comparing and analyzing five models, involving the Deep Neural Network, Light Gradient Boosting Machine, Random Forest, Gradient Boosting Decision Tree, and CatBoost, several key performance indicators such as accuracy, precision, recall, F1 score, generalization error, stability, robustness, and adaptability score are comprehensively investigated. The research results reveal that the optimization model proposed in this study is superior to the comparison model in most evaluation indicators, especially when the data volume reach

SUBMITTER: Tang S 

PROVIDER: S-EPMC11449294 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature