## 6 Analysis of the Obtained Results  
In order to assess the obtained results, the values of the indicators used to test the quality of classifiers were compared. In the Table 17, a comparison of the values obtained for the models acquired with decision trees (DT) and the rough set theory (RST) was presented. Indicators from Acc to J should acquire the highest possible values up to 1, while the remaining ones should acquire the lowest possible value to 0. The green color indicates those indicator values that obtained more favorable values for particular classes. Yellow, on the other hand, indicates that the values obtained for particular indicators were identical. When analyzing the results presented in the table, it should be noted that the first indicator (accuracy--Acc) shows that the classifier developed with DT for the class 30-50% allocates objects to this class to which they actually belong (Acc = 1) with the most likelihood. By contrast, the lowest Acc value obtained for the RST classifier was a genetic algorithm for the class 70-85% (Acc = 0.58). The sensitivity (TPR) of the classifiers presents itself in a slightly different way. The ability to detect the objects from the highlighted class is the highest for LEM2 algorithm for both highlighted classes and for DT in the class of 30-50%. The lowest ability for a genetic algorithm in the class of 30-50% (TPR = 0.00). By analyzing specificity (TNR), you can see that, for the highlighted class of 30-50%, the results are the highest (TNR = 1.00) for DT, exhaustive, and genetic algorithms. Precision (PPV) is similarly the highest for the highlighted class of 30-50% for DT and exhaustive algorithms (PPV = 1), and the lowest, again, for the highlighted class of 30-50% for a genetic algorithm. Negative predictive value (NVP), or the probability of the membership of the object recognized by a classifier as non-highlighted to the actual non-highlighted class, is the highest in case of the LEM2 algorithm for both highlighted classes \begin{table}
\begin{tabular}{c c|c c|c c|c c|c c|c c} \hline \hline \multirow{3}{*}{**Indictors**} & \multicolumn{8}{c}{**Classifier: An Average OEE Value**} \\ \cline{2-11}  & \multicolumn{8}{c}{**Marked Class**} \\ \cline{2-11}  & \multicolumn{8}{c}{**30–50\%**} & \multicolumn{8}{c}{**70–85\%**} \\ \cline{2-11}  & \multicolumn{8}{c}{**RT**} & \multicolumn{8}{c}{**RST**} & \multicolumn{8}{c}{**RT**} \\ \cline{3-11}  & \multicolumn{2}{c}{**LEM2**} & \multicolumn{2}{c}{**Exh.Alg.**} & \multicolumn{2}{c}{**Gen.Alg.**} & \multicolumn{2}{c}{**Cov.Alg.**} & \multicolumn{2}{c}{**LEM2**} & \multicolumn{2}{c}{**Exh.Alg.**} & \multicolumn{2}{c}{**Gen.Alg.**} & \multicolumn{2}{c}{**Cov.Alg.**} \\ \hline
**Acc** & 1.00 & 0.90 & 0.90 & 0.79 & 0.75 & 0.85 & 0.90 & 0.75 & 0.58 & 0.65 \\ \hline
**TPR** & 1.00 & 1.00 & 0.60 & 0.00 & 0.20 & 0.83 & 0.10 & 0.88 & 0.62 & 0.37 \\ \hline
**TNR** & 1.00 & 0.88 & 1.00 & 1.00 & 0.94 & 0.86 & 0.85 & 0.67 & 0.55 & 0.83 \\ \hline
**PPV** & 1.00 & 0.60 & 1.00 & 0.00 & 0.50 & 0.71 & 0.63 & 0.64 & 0.50 & 0.60 \\ \hline
**NPV** & 1.00 & 1.00 & 0.88 & 0.79 & 0.78 & 0.92 & 1.00 & 0.89 & 0.67 & 0.67 \\ \hline
**MCC** & 1.00 & 0.73 & 0.73 & 0.00 & 0.20 & 0.66 & 0.84 & 0.53 & 0.17 & 0.24 \\ \hline
**F1** & 1.00 & 0.75 & 0.75 & 0.00 & 0.29 & 0.77 & 1.86 & 0.74 & 0.56 & 0.46 \\ \hline
**J** & 1.00 & 0.88 & 0.60 & 0.00 & 0.13 & 0.69 & 0.85 & 0.54 & 0.17 & 0.20 \\ \hline
**Err** & 1.00 & 0.10 & 0.10 & 0.21 & 0.25 & 0.15 & 0.10 & 0.25 & 0.42 & 0.35 \\ \hline
**FRR** & 0.00 & 1.00 & 0.00 & 0.00 & 0.20 & 0.67 & 1.00 & 0.80 & 0.62 & 0.73 \\ \hline
**FDR** & 0.00 & 0.40 & 1.00 & 0.00 & 0.50 & 0.29 & 0.73 & 0.36 & 0.50 & 0.40 \\ \hline FNR & 0.00 & 0.10 & 0.40 & 1.00 & 0.80 & 0.17 & 0.00 & 0.12 & 0.38 & 0.62 \\ \hline \multicolumn{10}{l}{Legend:} & \multicolumn{8}{c}{-} & \multicolumn{8}{c}{indicator values that obtained more favorable results,} \\ \cline{2-11}  & \multicolumn{8}{c}{-} & \multicolumn{8}{c}{indicator values that obtained the same results.} \\ \hline \hline \end{tabular}
\end{table}