# Machine Learning Algorithms: An Experimental Evaluation for Decision Support Systems
## 5 Experimental Evaluation
### Human Resource Dataset  
Figure 14 shows the best results of supervised algorithms for _Accuracy_, _Precision_, _Recall_, _F1-Score_ and _ROC-AUC-Score_ metrics applied to the human resource test dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \begin{tabular}{c} **Logistic** \\ **Regression** \\ \end{tabular} & \begin{tabular}{c} **Preprocessing** \\ **Logistic** \\ **Regression** \\ \end{tabular} & \begin{tabular}{c} **Decision** \\ **Tree** \\ \end{tabular} & \begin{tabular}{c} **Preprocessing** \\ **Decision Tree** \\ \end{tabular} & \
\begin{tabular}{c} **Naïve Bayes** \\ **Naïve Bayes** \\ \end{tabular} &
\begin{tabular}{c} **Preprocessing** \\ **Naïve Bayes** \\ \end{tabular} \\ \hline Accuracy & 0.786 & 0.766 & 0.740 & 0.724 & 0.747 & 0.481 \\ \hline Precision & 0.694 & 0.657 & 0.557 & 0.592 & 0.571 & 0.368 \\ \hline Recall & 0.532 & 0.489 & 0.723 & 0.468 & 0.681 & 0.865 \\ \hline F1-Score & 0.602 & 0.561 & 0.630 & 0.523 & 0.621 & 0.516 \\ \hline ROC-AUC-Score & 0.715 & 0.689 & 0.736 & 0.657 & 0.728 & 0.582 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Logistic Regression, Decision Tree, Naive Bayes preprocessing the diabetes test dataset. Figure 13: _Homogeneity, Completeness, V Measure, Adjusted Rand_ and _Adjusted Mutual Info scores_ for unsupervised clustering algorithms of diabetes test dataset. The Random Forest algorithm obtained the best performance results in the metrics with an _Accuracy_ of 97.3%, _Precision_ of 90.8%, _F1-Score_ of 91.4% and _ROC-AUC-Score_ of 95.1%. For the metric _Recall_, the SVM linear algorithm presented the best result of 94.7%. On the other hand, the SVM linear algorithm presented the worst results for the metric _Accuracy_ of 79.7% and the metric _Precision_ of 43.4%, while the Logistic Regression had the worst results for the metrics _Recall_ of 24.3%, _F1-Score_ of 36.5% and _ROC-AUC-Score_ of 61.3%. Figure 15 presents the results of the _Silhouette_ Score metric for the unsupervised clustering algorithms applied to human resource test dataset. We can see that the BIRCH algorithm obtained the best _Silhouette_ Score value of 72.6% while DBSCAN had the worst _Silhouette_ Score value of 61.3%. The reason why DBSCAN had inferior results can be justified by the fact that in some cases, determining an appropriate distance of neighborhood (eps) is not easy and it requires domain knowledge. Additionally, if clusters are very different in terms of in-cluster densities, DBSCAN is not well suited to define clusters. Figure 14: _Accuracy, Precision, Recall, F1-Score_ and _ROC-AUC-Score_ for supervised algorithms of human resource test dataset. Figure 15: _Silhouette_ Score for the unsupervised clustering algorithm of human resource test dataset. Figure 16 shows the results for the metrics _Homogeneity_ Score, _Completeness_ Score, _V Measure_ Score, _Adjusted Mutual Info Score_ and _Adjusted Rand Score_ for the unsupervised clustering algorithms applied to the human resource test dataset. All the previous metrics had low scores which were distant from the best value of 1 for every unsupervised clustering algorithm, among which DBSCAN presented the worst results. As previously stated, DBSCAN can have a bad performance because it is normally difficult to determine an appropriate distance of neighborhood (eps) and if clusters are very different in terms of in-clusters densities. The best metric results of applying the two preprocessing unsupervised clustering techniques (K-Means and BIRCH) to Logistic Regression, Decision Tree and Naive Bayes supervised algorithms for human resource test dataset are shown in Table 2.  
The result of applying unsupervised clustering techniques as preprocessing of supervised algorithms considerably improved the performance, particularly for Logistic Regression algorithm, whereas _Accuracy_ changed from 86.8% to 96%, _Precision_ from 74% to 86.1%, _Recall_ from 36.5% to 87.5%, _F1-Score_ from 36.5% to 87.5% and _ROC-AUC-Score_ from 61.3% to 93.1%.