  Fifth-generation (5G) mobile communication networks have recently emerged in
various fields, including highspeed trains. However, the dense deployment of 5G
millimeter wave (mmWave) base stations (BSs) and the high speed of moving
trains lead to frequent handovers (HOs), which can adversely affect the
Quality-of-Service (QoS) of mobile users. As a result, HO optimization and
resource allocation are essential considerations for managing mobility in
high-speed train systems. In this paper, we model system performance of a
high-speed train system with a novel machine learning (ML) approach that is
nested cross validation scheme that prevents information leakage from model
evaluation into the model parameter tuning, thereby avoiding overfitting and
resulting in better generalization error. To this end, we employ ML methods for
the high-speed train system scenario. Handover Margin (HOM) and Time-to-Trigger
(TTT) values are used as features, and several KPIs are used as outputs, and
several ML methods including Gradient Boosting Regression (GBR), Adaptive
Boosting (AdaBoost), CatBoost Regression (CBR), Artificial Neural Network
(ANN), Kernel Ridge Regression (KRR), Support Vector Regression (SVR), and
k-Nearest Neighbor Regression (KNNR) are employed for the problem. Finally,
performance comparisons of the cross validation schemes with the methods are
made in terms of mean absolute error (MAE) and mean square error (MSE) metrics
are made. As per obtained results, boosting methods, ABR, CBR, GBR, with nested
cross validation scheme superiorly outperforms conventional cross validation
scheme results with the same methods. On the other hand, SVR, KNRR, KRR, ANN
with the nested scheme produce promising results for prediction of some KPIs
with respect to their conventional scheme employment.
