Stacked ensemble model for NBA game outcome prediction analysis.

ABSTRACT: This research presents a stacked ensemble approach that employs artificial intelligence (AI) techniques to predict the outcomes of NBA games. Several machine learning algorithms were utilized, including Naïve Bayes, AdaBoost, Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), XGBoost, Decision Tree, and Logistic Regression. The best-performing models were selected to serve as the base learners in the ensemble architecture. To improve the model's interpretability and transparency, SHAP was used to clarify its decision-making process. The model was trained and evaluated using publicly available NBA datasets from 2021-2022,2022-2023, and 2023-2024. Experimental results indicate that the proposed ensemble approach is practical in predicting game outcomes. Furthermore, the SHAP analysis pr

SUBMITTER: He G 

PROVIDER: S-EPMC12357926 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature