## 5 Post Analysis and Discussion  
Because the number of false positives is higher than the true positives it was decided to do a post analysis. For the analysis the best model was chosen (#16, see Table 3). First of all the distribution of false positives dependent on the potnumber is investigated, shown in Figure 7. It can be observed that the false positives are not uniformly distributed. Some potnumbers exhibit considerably more false positives than others. One reason for this is the accumulation of false positives in groups of consecutive time steps. Among the potrooms the differences are small. A further analysis on a sample basis regarding the prediction over time shows that some false positives are related to events like anode change, metal tapping and current drop. Disabling the model for 3 h after restart in consequence of a current drop and for 30 min after anode change can improve the score slightly. Disabling the model during other events does not increase the scoring. Removing false positives if the prediction is up to 30 min in advance also yields an improvement. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \# & **Features** & **Sampling** & **Best Validation** & **Best Model** & **F\({}_{1}\) [\%]** \\ \hline \multirow{2}{*}{7} & all features except catch22 (60 min, 5 min) & NearMiss-3 & \multirow{2}{*}{KFold} & Random & \multirow{2}{*}{17.8} \\  & and tsfeatures (60 min, 5 min) & \(IR=10\) & & Forest & \\ \hline \multirow{2}{*}{8} & all features except catch22 (60 min, 5 min) & NearMiss-3 & \multirow{2}{*}{KFold} & Random & \multirow{2}{*}{18.6} \\  & and tsfeatures (60 min, 5 min) & \(IR=11\) & & Forest & \\ \hline \multirow{2}{*}{9} & all features except catch22 (60 min, 5 min) & NearMiss-3 & \multirow{2}{*}{KFold} & Random & \multirow{2}{*}{18.7} \\  & and tsfeatures (60 min, 5 min) & \(IR=12\) & & Forest & \\ \hline \multirow{2}{*}{10} & all features except catch22 (60 min, 5 min) & NearMiss-3 & \multirow{2}{*}{KFold} & Random & \multirow{2}{*}{18.9} \\  & and tsfeatures (60 min, 5 min) & \(IR=13\) & & Forest & \\ \hline \multirow{2}{*}{11} & all features except catch22 (10 min, 1 min) & NearMiss-3 & \multirow{2}{*}{Stratified KFold} & Random & \multirow{2}{*}{3.9} \\  & and tsfeatures (10 min, 1 min) & \(IR=7\) & & Forest & \\ \hline \multirow{2}{*}{12} & all features & Random & \multirow{2}{*}{Stratified KFold} & Random & \multirow{2}{*}{0.7} \\  & undersampler & \(IR=7\) & & Forest & \\ \hline \multirow{2}{*}{13} & all features & NearMiss-1 & \multirow{2}{*}{all} & all & \multirow{2}{*}{0} \\  & & \(IR=7\) & & Forest & \\ \hline \multirow{2}{*}{14} & all features & NearMiss-3 & \multirow{2}{*}{KFold} & Random & \multirow{2}{*}{15.2} \\  & all features & \(IR=7\) & & Forest & \\ \hline \multirow{2}{*}{15} & all features & NearMiss-3 & \multirow{2}{*}{Stratified KFold} & Random & \multirow{2}{*}{14.4} \\  & all features & \(IR=8\) & & Forest & \\ \hline \multirow{2}{*}{16} & all features & NearMiss-3 & \multirow{2}{*}{KFold} & Random & \multirow{2}{*}{29.3} \\  & all features & \(IR=9\) & & Forest & \\ \hline \multirow{2}{*}{17} & all features & NearMiss-3 & \multirow{2}{*}{KFold} & Random & \multirow{2}{*}{15.7} \\  & all features & \(IR=10\) & & Forest & \\ \hline \multirow{2}{*}{18} & all features & NearMiss-3 & \multirow{2}{*}{KFold} & Random & \multirow{2}{*}{15.4} \\  & all features & \(IR=12\) & & Forest & \\ \hline \multirow{2}{*}{20} & all features & NearMiss-3 & \multirow{2}{*}{KFold} & Random & \multirow{2}{*}{15.3} \\  & all features & \(IR=13\) & & Forest & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._Finally the number of false positives decreases to 570 while the true positives reduce to 163 and therefore the F\({}_{1}\) score increases to 32.4%. Regarding the false positives again, the previously mentioned LVP-AEs need also to be taken into account.