# C-SHAP: A Hybrid Method for Fast and Efficient Interpretability
## 2 Related Works  
Feature selection is vital for improving the performance and interpretability of machine learning models by focusing on the most relevant variables, which simplifies models and reduces overfitting [39]. This process enhances efficiency and accuracy, particularly in critical fields like healthcare and finance, where model decisions must be transparent [40]. For example, in [41], feature selection methods such as LIME, SHAP, and CIU (Contextual Importance and Utility) were applied to datasets, including the Wisconsin Breast Cancer and SPECT (Single-Photon-Emission Computed Tomography) datasets, achieving execution times ranging from 116 s to 44,262 s.  
Interpretability has become essential in AI [22], highlighting its role in building trust, especially in high-stakes applications. Methods that make models more understandable without sacrificing performance are crucial [7]. These works underscore the importance of both feature selection and interpretability in modern AI systems. SHAP is a state-of-the-art method for feature attribution based on Shapley values from cooperative game theory, where Shapley values represent the fair contribution of each feature by quantifying its impact on the overall prediction based on all possible feature coalitions [42]. It provides local and global explanations, making it a powerful tool for understanding model behavior. SHAP's foundation in game theory ensures fairness by distributing the contribution of each feature to the final prediction [21]. This method hasbeen widely adopted due to its model-agnostic nature and ability to explain complex models [43]. One of SHAP's key strengths is its theoretical soundness, ensuring consistency in feature importance [14; 21]. In comparison, LIME approximates model behavior by constructing interpretable models around individual predictions, offering local explanations for complex models [22]. By generating simpler models, such as linear regressions, to explain a single prediction, LIME is often faster than SHAP. For example, LIME provided explanations for a text classification dataset with an average execution time of \(\sim\)3 s per instance for 5000 perturbations [22], whereas Kernel SHAP required significantly more computations for models like MNIST [21]. However, unlike LIME [22], SHAP provides more reliable feature importance rankings by guaranteeing that higher importance scores accurately reflect their contribution to the model's output. This consistency has made SHAP a preferred method in critical domains such as healthcare and finance, where interpretability and trustworthiness are essential [44; 45]. Despite its advantages in providing transparent and consistent feature attributions, SHAP is known for its computational inefficiencies, especially when dealing with large datasets or complex models like ensemble methods. The process of calculating Shapley values is inherently resource-intensive, as it requires evaluating all possible feature combinations to determine their contributions [42; 46]. This complexity grows exponentially with the number of features, making SHAP computationally expensive, particularly in models such as Random Forest or XGBoost, where numerous decision paths must be analyzed for each prediction [21]. For instance, [PERSON] et al. [14] observed that TreeSHAP's execution times ranged from 16.96 ms (Heart Disease) to 10,894.86 ms (Boston). Conversely, LIME achieved times as low as 0.23 s for simpler datasets [13]. Several studies have highlighted SHAP's performance limitations. For example, the work in [14] discusses how SHAP can become prohibitively slow for high-dimensional datasets due to its quadratic time complexity in model-agnostic cases. For example, kernel SHAP [23], the general purpose variant, is particularly slow compared to specialized versions like Tree SHAP, which is optimized for tree-based algorithms [43]. Benchmark studies have shown that the execution time for SHAP significantly increases with model depth and dataset size, particularly in XGBoost and Random Forest models [14].