# GlassBoost: A Lightweight and Explainable Classification Framework for Tabular Datasets
## 7 Conclusions  
This study introduced a novel explainable artificial intelligence (XAI) framework for the intrusion detection problem. It combined the capabilities of XGBoost in generating importance scores, specifically gain, to select the most important features. The selected features were utilised to generate decision tree classifiers that were both accurate and interpretable. GlassBoost achieves strong performance and model transparency, offering a practical alternative to complex black-box models. Its simplicity and low computational requirements make it well-suited for deployment in resource-constrained environments such as edge computing systems. Although the method demonstrates competitive performance on standard machine learning problems focused on tabular data, it inherits certain limitations from its underlying tree-based models. In particular, tree-based models, such as decision trees and gradient-boosting machines (GBMs), do not inherently capture temporal dependencies, which can be critical in time-series data that exhibits trends or seasonal variations. Consequently, the framework may be less effective in domains with a dominant temporal structure. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** \\ \hline
**GlassBoost (maximum depth = 4, \(d\) = 8)** & **0.9868** & **0.9843** & **0.9792** \\ Random Forest & 0.99 & 0.96 & 0.96 \\ AdaBoost & 0.93 & 0.78 & 0.78 \\ Deep Neural Network (DNN) & 0.94 & 0.80 & 0.80 \\ Support Vector Machine (SVM) & 0.99 & 0.97 & 0.97 \\ K-Nearest Neighbor (KNN) & 0.99 & 0.99 & 0.99 \\ Multi-Layer Perceptron (MLP) & 0.96 & 0.88 & 0.88 \\ LightGBM & 0.97 & 0.92 & 0.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of GlassBoost with XAI-IDS [23]. Furthermore, simplifying a model to enhance its transparency often comes at the cost of reduced predictive performance. However, a model's performance is also heavily dependent on the nature of the data it is trained on, as emphasised by the no free lunch theorem. In our approach, the baseline model is XGBoost, from which we identify the most informative and discriminative features using gain scores obtained during training. When a small subset of features accounts for the majority of the cumulative gain score, it suggests that model compression (smaller trees of smaller depth) can preserve most of the original predictive power and yield results comparable to those of the complete baseline model. Nevertheless, some loss in performance is still expected due to simplification. Future work should focus on applying the framework to similar problems/exploring its applicability across a broader range of anomaly detection and classification tasks. Furthermore, extending XGBoost to multi-class problems is indeed feasible. GlassBoost can be readily extended to multi-class classification tasks by training the baseline XGBoost model on a multi-class dataset. The gain scores used in our feature selection process are computed in the same way, and the subsequent model compression step remains straightforward. Since decision trees are inherently capable of handling multi-class problems, this extension aligns naturally with our approach. Conceptualization, E.N. ; methodology, E.N. ; software, E.N. ; writing--original draft preparation, E.N. ; writing--review and editing, E.N., J.B., C.R., and A.N.O. ; supervision, C.R. and J.B.; project administration, C.R. and J.B.; funding acquisition, C.R. and J.B. All authors have read and agreed to the published version of the manuscript. This research was funded by the Research Ireland Award, number 13/RC/2094_P2. Not applicable. The dataset used in this study is cited within the paper and is publicly available as mentioned within the paper. All source code developed for this study is also publicly available at: [[https://github.com/PatternCode/GlassBoost](https://github.com/PatternCode/GlassBoost)]([https://github.com/PatternCode/GlassBoost](https://github.com/PatternCode/GlassBoost)), accessed on 9 June 2025.