# A Random PRIM Based Algorithm for Interpretable Classification and Advanced Subgroup Discovery
## 4 Results
### Results
Regarding the four primary performance indicators--accuracy, recall, precision, and F1-score--R-PRIM-Cl consistently demonstrates excellent performance, often securing the highest scores in datasets like Cancer, Heart, and Credit. This demonstrates its proficiency in handling intricate datasets while maintaining interpretability. XGB (XG-Boost) performs exceptionally well in precision and F1-score, specifically in simpler datasets like Mush and TicTac, where it consistently achieves scores close to or at 100%. Random forest (RF) consistently delivers satisfactory results across all metrics, but it often falls behind R-PRIM-Cl and XGB in more challenging datasets. Lastly, logistic regression (LG) competes well, particularly in datasets such as Cancer and Credit, but it typically trails behind ensemble methods like XGB and RF, as well as R-PRIM-Cl, in most of the metrics. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**F1-Score**} & \multicolumn{4}{c}{**Accuracy**} \\ \hline
**Datasets** & **RF** & **XGB** & **LG** & **R-PRIM-Cl** & **RF** & **XGB** & **LG** & **R-PRIM-Cl** \\ \hline Vote & 96.45 & 96.77 & 95.89 & **97.00** & 96.56 & 97.10 & **97.43** & 97.40 \\ \hline Mush & 98.91 & **99.05** & 95.65 & 97.06 & **100.00** & **100.00** & 98.63 & 97.54 \\ \hline Cancer & 80.64 & 83.76 & 89.00 & **92.15** & 71.38 & 71.78 & 87.63 & **90.04** \\ \hline Heart & 89.59 & 90.30 & 84.32 & **91.82** & 80.20 & 83.56 & 92.13 & **96.15** \\ \hline TicTac & **98.06** & 98.00 & 97.33 & 97.43 & 98.85 & **98.87** & 97.86 & 98.37 \\ \hline Diabetes & 89.26 & 88.09 & 89.59 & **91.43** & 84.65 & 84.37 & **89.61** & 87.98 \\ \hline Credit & 89.12 & 83.87 & 91.56 & **92.56** & 90.15 & 89.61 & 91.34 & **92.42** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of the F1-score and accuracy of every algorithm on the seven datasets in %. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Recall**} & \multicolumn{4}{c}{**Precision**} \\ \hline
**Datasets** & **RF** & **XGB** & **LG** & **R-PRIM-Cl** & **RF** & **XGB** & **LG** & **R-PRIM-Cl** \\ \hline Vote & 94.33 & 94.33 & 93.33 & **95.32** & 98.67 & **99.33** & 98.60 & 98.74 \\ \hline Mush & **98.12** & **98.12** & 97.72 & 97.65 & 99.72 & **100.00** & 93.67 & 96.47 \\ \hline Cancer & 84.17 & 89.64 & 90.31 & **92.18** & 77.39 & 78.60 & 87.72 & **92.13** \\ \hline Heart & 92.01 & **94.37** & 78.36 & 93.57 & 87.30 & 86.57 & **91.25** & 90.14 \\ \hline TicTac & 97.35 & **98.12** & 96.58 & 98.06 & **98.79** & 97.88 & 98.10 & 96.80 \\ \hline Diabetes & 87.30 & 85.64 & 89.51 & **88.67** & 91.31 & 90.68 & 89.67 & **94.36** \\ \hline Credit & 89.64 & 82.45 & **91.35** & 90.03 & 88.60 & 85.34 & 91.78 & **95.24** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of the recall and precision of every algorithm on the seven datasets in %. With the highest scores in accuracy (97.50%), recall (95.32%), and F1-score (97.00%), R-PRIM-Cl performs better than other algorithms in the Vote dataset. Nonetheless, XGB maintains its competitiveness by producing comparable outcomes in terms of precision (99.33%) and other metrics. The Mush dataset shows that XGB can handle simpler datasets with well-separated classes, as evidenced by its perfect precision (100.00%) and superior performance across most metrics. This dataset consistently shows R-PRIM-Cl as the second-best performer, with impressive precision (96.47%) and F1-score (97.06%). The Cancer dataset demonstrates how well R-PRIM-Cl performs on increasingly difficult tasks, as it leads in every metric, including F1-score (92.15%), accuracy (90.04%), and precision (92.13%). LG does well in this dataset as well, outperforming RF and XGB by a wide margin across most metrics. Likewise, R-PRIM-Cl attains the highest F1-score (91.82%), precision (90.14%), and recall (93.57%) in the Heart dataset. With a robust F1-score (90.30%), XGB is still competitive, but it trails behind R-PRIM-Cl by a small margin in other metrics.