## 5 Parameter Tuning
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multicolumn{10}{c}{**Accuracy before Hyper-Tuning**} & \multicolumn{8}{c}{**Precision before Hyper-Tuning**} \\ \hline
**Datasets** & **GB** & **RF** & **DT** & **KNN** & **R-SVM** & **LR** & **GB** & **RF** & **DT** & **KNN** & **R-SVM** & **LR** \\ \hline IRIS & 94.74 & 97.37 & 94.74 & 92.11 & 92.11 & 92.11 & 93.39 & 96.02 & 93.39 & 90.76 & 90.76 & 90.76 \\ Glass & 94.59 & 95.32 & 91.35 & 94.11 & 94.11 & 94.11 & 93.24 & 93.97 & 90 & 92.76 & 92.76 & 92.76 \\ HAR & 88.45 & 87.3 & 86.2 & 86.32 & 86.32 & 86.32 & 87.1 & 85.95 & 84.85 & 84.97 & 84.97 & 84.97 \\ B\_Tissue & 95.71 & 96.57 & 92.86 & 88.92 & 88.92 & 88.92 & 94.36 & 95.22 & 91.51 & 87.57 & 87.57 & 87.57 \\ Car & 86.5 & 82.89 & 80.97 & 81.6 & 81.6 & 81.6 & 85.15 & 81.54 & 79.62 & 80.25 & 80.25 & 80.25 \\ CMC & 87.21 & 84.92 & 81.67 & 83 & 83 & 83 & 85.86 & 83.57 & 80.32 & 81.65 & 81.65 & 81.65 \\ Ecoli & 91.98 & 88.89 & 88.38 & 86.86 & 86.86 & 90.63 & 87.54 & 87.03 & 85.51 & 85.51 & 85.51 \\ Nursery & 82.94 & 83.67 & 81.99 & 88.57 & 88.57 & 88.57 & 81.59 & 82.32 & 80.64 & 87.22 & 87.22 & 87.22 \\ Opt\_Digits & 85.47 & 86.01 & 83.19 & 76.04 & 76.04 & 76.04 & 84.12 & 84.66 & 81.84 & 74.69 & 74.69 & 74.69 \\ Page\_Block & 85.85 & 83.37 & 84.41 & 79.27 & 79.27 & 79.27 & 84.5 & 82.02 & 83.06 & 77.92 & 77.92 & 77.92 \\ Satimage & 85.53 & 84.4 & 85.53 & 79.66 & 79.66 & 79.66 & 84.18 & 83.05 & 84.18 & 78.31 & 78.31 & 78.31 \\ Soyaben & 92.35 & 91.04 & 89.92 & 80.65 & 80.65 & 80.65 & 91 & 89.69 & 88.57 & 79.3 & 79.3 & 79.3 \\ Vehicle & 82.5 & 83.5 & 75.36 & 81.68 & 81.68 & 81.68 & 81.15 & 82.15 & 74.01 & 80.33 & 80.33 & 80.33 \\ Volcanoesa & 80.81 & 79 & 81.9 & 86.49 & 86.49 & 86.49 & 79.46 & 77.65 & 80.55 & 85.14 & 85.14 \\ Wine & 96.61 & 96.61 & 94.92 & 78.82 & 78.82 & 78.82 & 95.26 & 95.26 & 93.57 & 77.47 & 77.47 & 77.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy and precision before hyper-tuning. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Accuracy before Hyper-Tuning**} & \multicolumn{6}{c}{**Precision before Hyper-Tuning**} \\ \hline
**Datasets** & **GB** & **RF** & **DT** & **KNN** & **R-SVM** & **LR** & **GB** & **RF** & **DT** & **KNN** & **R-SVM** & **LR** \\ \hline IRIS & 90.5 & 93.13 & 90.5 & 87.87 & 87.87 & 87.87 & 87.03 & 89.66 & 87.03 & 84.4 & 84.4 & 84.4 \\ Glass & 90.35 & 91.08 & 87.11 & 89.87 & 89.87 & 89.87 & 86.88 & 87.61 & 83.64 & 86.4 & 86.4 & 86.4 \\ HAR & 84.21 & 83.06 & 81.96 & 82.08 & 82.08 & 82.08 & 80.74 & 79.59 & 78.49 & 78.61 & 78.61 & 78.61 \\ B\_Tissue & 91.47 & 92.33 & 88.62 & 84.68 & 84.68 & 84.68 & 88 & 88.86 & 85.15 & 81.21 & 81.21 & 81.21 \\ Car & 82.26 & 78.65 & 76.73 & 77.36 & 77.36 & 77.36 & 78.79 & 75.18 & 73.26 & 73.89 & 73.89 & 73.89 \\ CMC & 82.97 & 80.68 & 77.43 & 78.76 & 78.76 & 78.76 & 79.5 & 77.21 & 73.96 & 75.29 & 75.29 & 75.29 \\ Ecoli & 87.74 & 84.65 & 84.14 & 82.62 & 82.62 & 82.62 & 82.62 & 84.27 & 81.18 & 80.67 & 79.15 & 79.15 & 79.15 \\ Nursery & 78.77 & 79.43 & 77.75 & 84.33 & 84.33 & 84.33 & 75.23 & 75.96 & 74.28 & 80.86 & 80.86 & 80.86 \\ Opt\_Digits & 81.23 & 81.77 & 78.95 & 71.8 & 71.8 & 71.8 & 77.76 & 78.3 & 75.48 & 68.33 & 68.33 & 68.33 \\ Page\_Block & 81.61 & 79.13 & 80.17 & 75.03 & 75.03 & 75.03 & 75.03 & 78.14 & 75.66 & 76.7 & 71.56 & 71.56 \\ Satimage & 81.29 & 80.16 & 81.29 & 75.42 & 75.42 & 75.42 & 77.82 & 76.69 & 77.82 & 71.95 & 71.95 & 71.95 \\ Soyaben & 88.11 & 86.8 & 85.68 & 76.41 & 76.41 & 76.41 & 84.64 & 83.33 & 82.21 & 72.94 & 72.94 & 72.94 \\ Vehicle & 78.26 & 79.26 & 71.12 & 77.44 & 77.44 & 77.47 & 77.59 & 67.65 & 73.97 & 73.97 & 73.97 \\ Volcanoesa & 76.57 & 74.76 & 77.66 & 82.25 & 82.25 & 82.25 & 73.1 & 71.29 & 74.19 & 78.78 & 78.78 & 78.78 \\ Wine & 92.37 & 92.37 & 90.68 & 74.58 & 74.58 & 74.58 & 88.9 & 88.9 & 87.21 & 71.11 & 71.11 & 71.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Recall and F1-score before hyper-tuning.