# GlassBoost: A Lightweight and Explainable Classification Framework for Tabular Datasets
## 2 Explainability: Concepts and Categorisation  
In this study, we develop an effective XAI method tailored towards tabular data and investigate its effectiveness through its application to the intrusion detection problem in data networks. This section, therefore, focuses explicitly on the categorisation of the explainability relevant to tabular data [11,12]. We begin by reviewing key concepts and categorisations of explainability, followed by a brief overview of GlassBoost and its alignment within this context. One standard categorisation divides explanations into two classes: model-agnostic explanations and model-specific explanations [10]. Model-specific explanations leverage a particular model's unique features and characteristics, making them inherently tailored to specific architectures and unsuitable for broader application across different model types. In contrast, model-agnostic explanations are designed to be universally applicable, providing interpretability across various models regardless of their internal structure or underlying mechanisms. Another categorisation distinguishes between local and global explanations [10]. Local explanations aim to interpret a model's prediction for a specific instance, focusing solely on the rationale behind that prediction without considering the model's overall decision-making policy. Conversely, global explanations aim to understand the model's overall decision-making process comprehensively. A third categorisation divides the explanation methods into five different types: explanation by simplification [13], explanation by relevance of features [14], visual explanation [15,16], explanation by concept [17], and explanation by example [11,18]. Explanation by simplification approaches involves simplifying complex models into more understandable forms. Techniques like rule extraction and model distillation create simpler models that approximate the behaviour of the original complex model. The goal is to make the decision-making process more transparent and easier to understand. Explanation by relevance focuses on identifying and ranking the importance (or relevance) of different features in the model's decision-making process, highlighting which features most influence the model's predictions. Visual explanations utilise visual cues to illustrate the reasoning behind a model's predictions, whereas explanations by concept explain model decisions using high-level concepts that are understandable to humans. Finally, example-based approaches aim to identify data points near the target data point. These methods provide explanations by presenting data points that share the same prediction as the target data point or those with predictions that differ from it [18]. Glass and black-box models are among the most frequently used terminologies in XAI research. Inherently interpretable models, such as linear regression and decision trees, are often referred to as \"glass-box\" models because their internal workings are transparent to humans. These models can typically be described mathematically or visually, making their decision processes relatively easy to understand. They also enable users to trace the data flow from inputs to outputs, providing clear insights into how decisions are made. Black-box models, such as deep neural networks, are comparatively complex and opaque. Often, these models are too complex for human-level description, either by mathematics or visualisation. Explainability techniques often interpret these black-box models, providing insight into their decision-making processes. The method presented in this work employs a gradient-boosting machine (GBM) as a high-performance black-box model. Initially, the GBM is trained on the provided dataset. Subsequently, feature importance scores, which quantify a feature's contribution to the model, are computed (see Section 3). These scores effectively capture the extent to which a given feature enhances the model's predictive performance.