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IEEE/ICACT20260091 Slide.17        [Big Slide]	Oral Presentation
	Slide 17 presents the performance results for our ping-pong prediction models. The key findings are: Tree-based models, particularly XGBoost, outperform other approaches like SVM, Logistic Regression, and Deep Neural Networks on this dataset. XGBoost achieves the highest F1 score of 96.85%. The feature importance analysis reveals that temporal features like "time since last UE handover" and "time since last UE ping-pong" are among the most predictive variables. Cluster-level features such as "cluster ID" also rank highly, confirming the value of region-specific modeling. These results validate the effectiveness of our ML-driven approach to ping-pong prediction. By leveraging a diverse set of temporal, spatial, and contextual signals, the XGBoost model can accurately identify high-risk handovers and enable proactive mitigation. The strong performance across key metrics like accuracy, precision, and recall demonstrates the systems readiness for real-world deployment in the NWDAF to optimize 5G mobility management at scale.

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