# C-SHAP: A Hybrid Method for Fast and Efficient Interpretability
## 6 Results  
In the experiments, we utilized the datasets introduced in Section 5.1 to evaluate the performance of the proposed C-SHAP method in comparison to standard SHAP and LIME. The metrics used to assess performance include accuracy and execution time, presented in tables along with Venn diagrams, to demonstrate the feature selection efficiency of SHAP and C-SHAP. In the following Venn diagrams, we illustrate the number of features  
Figure 5: Comparison of execution times for SHAP and C-SHAP across dataset sizes on the diabetes dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset Size (\%)** & **SHAP Execution Time (Sec)** & **C-SHAP Execution Time (Sec)** \\ \hline \hline
10\% & 43.12 & 0.15 \\ \hline
25\% & 126.45 & 0.28 \\ \hline
50\% & 269.87 & 0.40 \\ \hline
75\% & 382.43 & 0.55 \\ \hline
100\% & 439.15 & 0.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Execution times of SHAP and C-SHAP on the Diabetes dataset for different dataset sizes. identified as the most important by each of the two methods considered, highlighting the overlap and differences in selected features between SHAP and C-SHAP. The results include accuracy to showcase how effectively the selected algorithm performs on the dataset. While accuracy assesses overall model performance rather than feature interpretability, it is not directly affected by methods like LIME, SHAP, or C-SHAP. These methods focus on explaining and interpreting feature importance rather than influencing accuracy. However, accuracy does underscore the importance of selecting relevant features, as effective feature selection contributes to optimal outcomes and enhances the accuracy of classification and regression tasks, ultimately leading to more robust and reliable predictive models. The Venn diagrams confirm that C-SHAP not only reduces execution time but also maintains interpretability by selecting relevant features, highlighting the efficiency gained through clustering in the C-SHAP method. The results demonstrate that C-SHAP significantly improves execution time while delivering performance comparable to SHAP. In the Venn diagrams, three colors represent feature selection: red indicates features chosen by SHAP, blue represents features chosen by C-SHAP, and green shows the mutual features selected by both SHAP and C-SHAP in each algorithm and dataset. The numbers within each section indicate the count of selected features in each category. In this experiment, we utilized the default settings of the SHAP library in Python, [[https://shap.readthedocs.io/en/latest/](https://shap.readthedocs.io/en/latest/)]([https://shap.readthedocs.io/en/latest/](https://shap.readthedocs.io/en/latest/)) (accessed on 14 November 2024). SHAP selects features by calculating SHAP values, which measure each feature's contribution to the prediction. Features with higher absolute SHAP values are considered more influential, allowing for feature ranking based on their impact on the model's decisions. In the Diabetes dataset (Table 4), C-SHAP significantly reduces the execution time for SVC, bringing it down from 1968.09 s to just 0.21 s, underscoring its efficacy in regression tasks. As illustrated in Figure 6, both SHAP and C-SHAP consistently identified the same two out of ten features as the most influential. Figure 6: The number of features in the Diabetes dataset (total number of features is 10) extracted by SHAP and C-SHAP and their mutual features. Similarly, for the Heart Disease dataset, in a classification task (Table 5), C-SHAP decreased the Random Forest execution time from 439.15 s to 0.29 s. Figure 7 confirms that SHAP and C-SHAP converge on the selection of the same two most significant features out of thirteen.