# C-SHAP: A Hybrid Method for Fast and Efficient Interpretability
## 1 Introduction
For example, LIMEs for text classification tasks can vary significantly, with execution times averaging 3 s per instance for 5000 perturbations, but with less stability compared to SHAP [22]  
Clustering has long been used in feature selection approaches to enhance the efficiency of machine learning models by grouping similar data points, reducing dimensionality, and improving computational performance [35]. The clustering of K-means, in particular, helps identify and cluster similar instances, allowing for a more streamlined feature selection process by focusing on representative clusters rather than every individual data point [36]. This strategy has proven effective in reducing redundancy and computational overhead, especially in high-dimensional datasets where traditional feature selection techniques become infeasible [31; 37]. Building on this concept, we propose C-SHAP, a novel approach that integrates K-means clustering to optimize SHAP's feature attribution process. SHAP, while robust and theoretically sound, becomes impractical for large datasets and complex models due to its high computational cost. C-SHAP overcomes this limitation by clustering similar data points, thereby reducing the number of calculations required for feature attributions. This not only significantly decreases the execution time but also preserves the interpretability and accuracy that make SHAP valuable. The motivation behind C-SHAP lies in its ability to provide scalable and fast explanations, making it suitable for real-time and large-scale applications and filling a critical gap in existing interpretability methods. Furthermore, our approach, which combines SHAP's interpretability with K-means clustering, enables scalable, efficient interpretability tailored to large and intricate datasets found in software product lines. This work emphasizes the growing intersection between machine learning interpretability and software engineering, reflecting broader trends in applying advanced analytics to improve software reliability and performance [38]. Therefore, this paper makes the following contributions:  
1. We present C-SHAP, a novel extension of SHAP, which integrates K-means clustering to significantly reduce execution time across various machine learning models. For example, on the Diabetes dataset, which was collected by the National Institute of Diabetes and Digestive and Kidney Diseases [[https://www.kaggle.com/datasets/antoniofurioso/diabetes-dataset-sklearn](https://www.kaggle.com/datasets/antoniofurioso/diabetes-dataset-sklearn)]([https://www.kaggle.com/datasets/antoniofurioso/diabetes-dataset-sklearn](https://www.kaggle.com/datasets/antoniofurioso/diabetes-dataset-sklearn)) (accessed on 14 November 2024), C-SHAP reduced the Random Forest execution time from 421.29 s to 0.39 s.
2. C-SHAP closely preserves select features of SHAP while significantly improving computational efficiency across various models. The Venn diagram analysis revealed that in models such as Random Forest, SVC (Support Vector Classifier), K-Nearest Neighbors, and Logistic Regression, the selected features between SHAP and C-SHAP were identical, with the only difference observed in the XGboost model. 3. C-SHAP was benchmarked against SHAP and LIME across multiple machine learning algorithms, demonstrating its versatility and efficiency improvements for both classification and regression tasks. The evaluations included datasets from domains such as healthcare and finance, confirming C-SHAP's scalability across diverse real-world applications. This paper is structured as follows: It begins with a review of related works in Section 2, providing an overview of previous research and methods relevant to SHAP and feature interpretability. Next, Section 3 explains how SHAP interprets model decisions. Following this, Section 4 introduces C-SHAP, detailing how it enhances SHAP's performance by improving computational efficiency, supported by formulas and examples.