# C-SHAP: A Hybrid Method for Fast and Efficient Interpretability
## 1 Introduction  
Machine learning models are increasingly applied in high-stakes domains such as healthcare and finance, where decisions must be transparent and accountable. However, many models function as black boxes, making it difficult to understand their decision-making processes, which is problematic when trust and interpretability are essential [1, 2, 3, 4]. In healthcare, AI-driven diagnostics must be interpretable for clinicians to ensure safe treatments [5, 6], while in finance, regulatory transparency is critical [7, 8, 9, 10, 11, 12]. For example, SHAP for Random Forest models in diabetes prediction required over 421 s on average, emphasizing the need for computationally efficient methods in time-critical domains like healthcare [13, 14]. Furthermore, as machine learning advances into specialized fields such as software product-line engineering, the demand for models that are both interpretable and computationally efficient is more pressing than ever [15, 16, 17]. In this field, predictive models play a crucial role in tasks like defect prediction, feature selection, and system performance optimization, all of which require transparency and accountability to ensure reliable decision-making [18]. However, traditional interpretability methods often struggle with the high-dimensional and complex data structures typical in software engineering, where balancing execution speed and model clarity against the complexity of configurations is essential [19, 20]. To address these issues, model interpretability methods like SHAP [21] and LIMEs (Local Interpretable Model-agnostic Explanations) [22] have been developed. SHAP has become a leading method for model interpretation by providing local and global feature attributions based on Shapley values from cooperative game theory [23]. In this context, Shapley values offer a way to fairly allocate a model's output among its features, akin to how they are used in cooperative game theory to distribute payouts to players based on their contributions to a coalition. This approach allows SHAP to explain the contribution of each feature to the prediction, making it a powerful tool for understanding both individual predictions and the model's overall behavior [24]. However, SHAP's computational complexity remains a significant obstacle; for instance, kernel SHAP for high-dimensional datasets can require thousands of perturbations, leading to execution times exceeding 32,000 s in certain cases [14]. However, SHAP's computational complexity presents a significant obstacle, especially for large-scale data and complex models like Random Forest and XGBoost (XGB), where the need to evaluate all possible feature combinations results in slow execution times [14; 25; 26; 27]. This challenge is exacerbated in real-time applications, such as fraud detection or personalized healthcare, where quick decision-making is essential [28; 29]. As a result, there is a pressing need for more efficient interpretability methods that balance computational cost and model accuracy. LIMEs, while offering faster explanations by approximating models locally, compromise on global interpretability and accuracy [22; 30; 31]. Although LIMEs efficiently create local surrogate models, such as linear regressions, around individual predictions, their local approximations often fail to capture the global behavior of the model, leading to inconsistencies in feature attributions across different predictions [22; 30; 32; 33]. Additionally, LIMEs can be unstable, with even slight changes in the input data potentially leading to different feature attributions, making them less reliable for domains like healthcare and finance, where both interpretability and accuracy are critical [31; 34].