# GlassBoost: A Lightweight and Explainable Classification Framework for Tabular Datasets
## 6 Discussion  
This study introduced a novel, explainable model that can be applied to tabular data in general, with its effectiveness demonstrated through a case study in intrusion detection. This section focuses on its empirical impact and broader implications. GlassBoost compresses a high-performing XGBoost model into a simpler decision tree that retains most of its predictive power while providing interpretability through transparent IF-THEN rules. Figure 11: SHAP summary plot illustrating feature importance for the baseline XGBoost classifier. As discussed in Section 4.3, the original XGBoost model trained on 62 input features achieved outstanding performance (accuracy: 0.9960, precision: 0.9921, and recall: 0.9970) using 50 boosting rounds and a maximum depth of four per tree. However, this model is inherently complex and difficult to interpret, both due to its size and the ensemble nature of boosted trees. To address this, we utilised the gain scores from XGBoost to rank feature importance and selected the top \(d\) features for training a set of decision trees. This technique enables model simplification while maintaining performance, making it suitable for constrained or transparency-critical environments. For instance, a decision tree with a maximum depth of four using just eight features yielded an accuracy of 0.9835, outperforming many XAI models reported in [37] (see Table 6). Only AdaBoost, XGBoost, and gradient boosting achieved higher accuracy but with fewer transparent model structures. However, other metrics, such as precision and recall, were not reported in [37], and the authors' exact definition of gradient boosting is unclear. For those techniques where the decision tree of depth four was less accurate, the preliminary XGBoost model, used to extract the gain values, provided better accuracy. Moreover, the number of features used in [37] was 15, while the decision tree of depth 4 uses only 8 features. A more detailed comparison with the models in [23], who also used the same dataset and identified their top features via SHAP, is presented in Table 7. Our ClassBoost model (depth four, \(d=8\)) achieved high scores across all key metrics (accuracy (0.9868), precision (0.9843), and recall (0.9792)), comparing favourably to state-of-the-art models, including random forest, SVM, and KNN. While the accuracy of these models was marginally higher (0.99), our approach provides explainability with minimal sacrifice in predictive performance. It also demonstrates robustness across key metrics, suggesting suitability for operational use where both precision and recall are critical. Moreover, it outperformed more complex methods such as DNN and MLP in all three metrics. GlassBoost's advantage lies not only in its competitive performance but also in its simplicity. Unlike black-box models that rely on post hoc interpretability tools, our approach is inherently explainable, providing direct insight into the decision logic. This is especially valuable in domains where transparency, auditability, and user trust are paramount, such as security-critical or regulated environments. An additional strength of GlassBoost is its computational efficiency. With low memory and processing requirements, it offers a reliable and effective solution for edge computing [38]. In this context, smart edge devices are deployed at the network's edge, close to mobile devices or sensors, where computational resources are often limited. In such scenarios, implementing a fast, accurate, and explainable model is crucial for the early detection of anomalies. To support this claim, we refer readers to [29], which demonstrates that when a CART tree is grown to a uniform depth of \(D\), i.e., to \(2^{D}\) terminal nodes, the sort