# Combining Machine Learning and Social Network Analysis to Reveal the Organizational Structures
## 4 Discussion
**} \\ \cline{2-11}  & & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** & **60\%** & **70\%** & **80\%** & **90\%** & **100\%** \\ \hline \multirow{3}{*}{**F-score macro average for the Decision Tree classification of three levels of the hierarchy in the Enron dataset. The values which are bolded are the best results for a given minimum communication activity. **} \\ \cline{2-11}  & & **1 month** & 0.3607 & 0.4308 & 0.4644 & 0.4448 & 0.5035 & 0.4934 & 0.5452 & **0.5682** & 0.5596 & 0.5171 \\ \cline{2-11}  & **2 months** \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Min. Activity**} \\ \hline  & & **1 month** & **2 months** & **3 months** & **4 months** & **5 months** \\ \hline \multirow{2}{*}{Decision Tree} & max\_depth & 4 & 8 & 8 & 10 & 5 \\ \cline{2-6}  & max\_features & 3 & 2 & 1 & 9 & 1 \\ \hline \multirow{2}{*}{Random Forest} & max\_depth & 8 & 6 & 6 & 10 & 6 \\ \cline{2-6}  & max\_features & 2 & 2 & 12 & 3 & 2 \\ \cline{2-6}  & n\_estimators & 32 & 16 & 16 & 16 & 8 \\ \hline \multirow{2}{*}{Neural Network} & alpha & 0.0001 & 0.01 & 0.0001 & 0.001 \\ \cline{2-6}  & hidden\_layer\_sizes & (13, 4) & (13) & (13, 9, 4) & (13, 9) & (13, 9) \\ \hline \multirow{2}{*}{SVM} & degree & 20 & 20 & 20 & 15 & 20 \\ \cline{2-6}  & C & 150 & 200 & 200 & 200 & 200 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: The best values of the hyperparameters for the models which uses the full set of features for the manufacturing company dataset with two hierarchy levels. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** & **60\%** & **70\%** & **80\%** & **90\%** \\ \hline \multirow{4}{*}{Neural Network} & 1 month & 0.2826 & 0.3044 & 0.3739 & 0.4514 & 0.4730 & 0.5517 & 0.5160 & **0.5654** & 0.5596 \\ \cline{2-11}  & 2 months & 0.2874 & 0.3193 & 0.3708 & 0.3933 & 0.4393 & 0.5084 & 0.5426 & 0.5333 & **0.6746** \\ \cline{2-11}  & 3 months & 0.2593 & 0.3368 & 0.3681 & 0.4007 & 0.4284 & **0.5915** & 0.5753 & 0.5166 & 0.5000 \\ \cline{2-11}  & 4 months & 0.2687 & 0.3077 & 0.3628 & 0.4031 & 0.5094 & 0.5105 & 0.6427 & 0.6476 & **0.7302** \\ \cline{2-11}  & 5 months & 0.2725 & 0.2774 & 0.3542 & 0.3489 & 0.3954 & 0.4832 & 0.4593 & 0.4551 & **0.6111** \\ \hline \hline \end{tabular}
\end{table} 
Table 17: F-score macro average for the collective classification of three levels of the hierarchy in the Enron dataset. The values which are bolded are the best results for a given minimum communication activity. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Percentage of the Known Nodes**} \\ \hline  & & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** & **60\%** & **70\%** & **80\%** & **90\%** \\ \hline \multirow{4}{*}{Random Forest} & 1 month & 0.2826 & 0.3044 & 0.3739 & 0.4514 & 0.4730 & 0.5517 & 0.5160 & **0.5654** & 0.5596 \\ \cline{2-11}  & 2 months & 0.2874 & 0.3193 & 0.3708 & 0.3933 & 0.4393 & 0.5084 & 0.5426 & 0.5333 & **0.6746** \\ \cline{2-11}  & 3 months & 0.2593 & 0.3368 & 0.3681 & 0.4007 & 0.4284 & **0.5915** & 0.5753 & 0.5166 & 0.5000 \\ \cline{2-11}  & 4 months & 0.2687 & 0.3077 & 0.3628 & 0.4031 & 0.5094 & 0.5105 & 0.6427 & 0.6476 & **0.7302** \\ \cline{2-11}  & 5 months & 0.2725 & 0.2774 & 0.3542 & 0.3489 & 0.3954 & 0.4832 & 0.4593 & 0.4551 & **0.6111** \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Hyperparameter search space for supervised learning algorithms.