# C-SHAP: A Hybrid Method for Fast and Efficient Interpretability
## 6 Results
\begin{table}
\begin{tabular}{l c c c c} \hline
**Model** & **Accuracy** & **LIME (Sec)** & **SHAP (Sec)** & **C-SHAP (Sec)** \\ \hline Random Forest & 0.73 & 0.14 & 421.29 & 0.39 \\ \hline SVC & 0.73 & 0.19 & 1968.09 & 0.21 \\ \hline XGBoost & 0.72 & 0.12 & 0.06 & 0.05 \\ \hline LR & 0.73 & 0.06 & 165.72 & 0.05 \\ \hline K-NNs & 0.68 & 0.09 & 350.84 & 0.07 \\ \hline \end{tabular}
\end{table} 
Table 4: Diabetes dataset performance, presenting accuracy metrics and execution times (seconds) for LIME, SHAP, and C-SHAP. \begin{table}
\begin{tabular}{l c c c c} \hline
**Model** & **Accuracy** & **LIME (Sec)** & **SHAP (Sec)** & **C-SHAP (Sec)** \\ \hline Random Forest & 0.736 & 0.09 & 439.15 & 0.29 \\ \hline SVC & 0.73 & 0.20 & 1984.44 & 0.22 \\ \hline XGBoost & 0.72 & 0.11 & 0.06 & 0.05 \\ \hline LR & 0.73 & 0.07 & 176.95 & 0.07 \\ \hline K-NNs & 0.68 & 0.08 & 363.78 & 0.07 \\ \hline \end{tabular}
\end{table} 
Table 5: Heart Disease dataset performance, presenting accuracy metrics and execution times (seconds) for LIME, SHAP, and C-SHAP. Figure 7: The number of features in the Heart Disease dataset (total number of features is 13) extracted by SHAP and C-SHAP and their mutual features. In another classification task involving the Indian Liver Disease dataset (Table 6), C-SHAP reduced the execution time for Random Forest from 953.18 s to 0.39 s. Figure 8 shows the consistent selection of the same two critical features out of ten by both SHAP and C-SHAP. For the Breast Cancer dataset (Table 7), which was also used for a classification task, C-SHAP cut down the execution time from 525.33 to 0.43 s for Random Forest and from 1044.86 to 0.15 s for SVC. Both methods identified the same two key features out of thirty, as depicted in Figure 9. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Accuracy** & **LIME (Sec)** & **SHAP (Sec)** & **C-SHAP (Sec)** \\ \hline Random Forest & 0.74 & 0.12 & 953.18 & 0.39 \\ \hline SVC & 0.73 & 0.20 & 3954.12 & 0.72 \\ \hline XGBoost & 0.70 & 0.14 & 0.05 & 0.04 \\ \hline LR & 0.72 & 0.12 & 368.42 & 0.17 \\ \hline K-NNs & 0.69 & 0.11 & 844.85 & 0.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Indian Liver Disease dataset performance, presenting accuracy metrics and execution times (seconds) for LIME, SHAP, and C-SHAP. Figure 8: The number of features in the Indian Liver Disease dataset (total number of features is 10) extracted by SHAP and C-SHAP and their mutual features. In the Wine dataset (Table 8), which is frequently used for classification, C-SHAP reduced the Random Forest execution time from 132.42 s to 0.58 s and the SVC execution time from 300.64 s to 0.69 s. Figure 10 illustrates the consistent selection of three influential features out of thirteen by both SHAP and C-SHAP. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Accuracy** & **LIME (Sec)** & **SHAP (Sec)** & **C-SHAP (Sec)** \\ \hline Random Forest & 0.98 & 0.12 & 525.33 & 0.43 \\ \hline SVC & 0.97 & 0.10 & 1044.86 & 0.15 \\ \hline XGBost & 0.95 & 0.13 & 0.03 & 0.04 \\ \hline LR & 0.95 & 0.09 & 245.18 & 0.07 \\ \hline K-NNs & 0.97 & 0.10 & 894.58 & 0.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Breast Cancer dataset performance, presenting accuracy metrics and execution times (seconds) for LIME, SHAP, and C-SHAP. Figure 9: The number of features in the Breast Cancer dataset (total number of features is 30) extracted by SHAP and C-SHAP and their mutual features. Lastly, for the Vertebral Column dataset (Table 9), C-SHAP decreased the Random Forest execution time from 19.07 s to 0.36 s and the SVC execution time from 41.53 s to 0.11 s. Both methods consistently identified three critical features out of six, as seen in Figure 11.  
Figure 11: The number of features in the Vertebral Column dataset (total number of features is 6) extracted by SHAP and C-SHAP and their mutual features. Figure 10: The number of features in the Wine dataset (total number of features is 13) extracted by SHAP and C-SHAP and their mutual features.