Abstract
This study investigates the classification of 5G network slice types using the public ”Network Slicing in 5G”dataset. Weaddressacritical scientific pitfall by identifying and mitigating evaluation leakage caused by near-deterministic rule-encoded binary indicators and heavy data duplication. By excluding these artificial context flags and utilizing only a minimal set of QoS-observable telemetry — specifically packet delay, packet loss rate, time, and equipment category — we establish a rigorous, leakage-aware evaluation protocol. Five representative classifier families were evaluated using a group-safe splitting strategy to ensure results reflect real-world operational conditions. Our experimental results demonstrate that tree-based ensembles significantly outperform linear models, with the strongest ensembles reaching about 95% accuracy (Histogram-based Gradient Boosting at 94.74% and Extra Trees marginally higher at 95.14%). This research underscores that while measurable network telemetry provides sufficient signal for high-accuracy slice recognition, non linear models are necessary to navigate the complex, stochastic overlaps inherent in real wireless environments.