## 4 Results
### Result Summary  
Table 4 presents a summary of 13 dataset results. Each experiment contains two model analyses (KNN and RF) on a variety of measures (recall, F1, etc. ). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & **Features** & **KNN Accuracy** & **KNN Precision** & **KNN Recall** & **KNN F1** & **RF Accuracy** & **RF Precision** & **RF Recall** & **RF F1** \\ \hline
86 & 9 & 0.931378 & 0.931829 & 0.931378 & 0.931386 & 0.951906 & 0.952107 & 0.951906 & 0.951898 \\ \hline
87 & 8 & 0.950147 & 0.950698 & 0.950147 & 0.950026 & 0.957771 & 0.958516 & 0.957771 & 0.957631 \\ \hline
88 & 7 & 0.953079 & 0.953313 & 0.953079 & 0.952945 & 0.958944 & 0.95899 & 0.958944 & 0.95872 \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & **Features** & **KNN Accuracy** & **KNN Precision** & **KNN Recall** & **KNN F1** & **RF Accuracy** & **RF Precision** & **RF Precision** & **Recall** & **RF F1** \\ \hline
0 & 11 & 0.972125 & 0.972116 & 0.972125 & 0.972108 & 0.977243 & 0.977243 & 0.977243 & 0.977227 \\ \hline
1 & 10 & 0.97169 & 0.971708 & 0.97169 & 0.971657 & 0.977461 & 0.977472 & 0.977461 & 0.977444 \\ \hline
2 & 9 & 0.977025 & 0.977022 & 0.977025 & 0.97702 & 0.98149 & 0.981481 & 0.98149 & 0.981483 \\ \hline
3 & 8 & 0.984321 & 0.984318 & 0.984321 & 0.984317 & 0.982905 & 0.982916 & 0.982905 & 0.982904 \\ \hline
4 & 7 & 0.986172 & 0.986177 & 0.986172 & 0.986171 & 0.985845 & 0.985846 & 0.985845 & 0.985844 \\ \hline
5 & 6 & 0.989765 & 0.989788 & 0.989765 & 0.989772 & 0.988676 & 0.988674 & 0.988676 & 0.988673 \\ \hline \end{tabular}
\end{table} 
Table 3: Algorithm results for ”gas turbine co and rox emission” dataset. Figure 2: Elbow and Silhouette plots for the “gas turbine co and rox emission” dataset. Each dataset in Table 4 contains two rows per dataset: the first row presents the analysis of the \"original\" full dataset (with all the Features after preprocessing); for example, in the case of the WineQualitywhite dataset, the full analysis was performed on 11 features after preprocessing. The second row presents the \"reduced\" dataset results after the method analysis; for example, in the WineQualitywhite use case, the second row describes the results on three features. Analysis of Table 5 reveals compelling results across our dataset experiments. In nine datasets (the first nine rows), we observed performance improvements despite using fewer features. For the remaining four datasets (the last four rows), we achieved substantial feature reduction with only negligible performance decreases. For instance, in the 'Anuran Calls' dataset, we reduced the feature count by nearly 60% (from 22 to 9 features) while sacrificing only 0.33% in KNN accuracy. In our computational performance analysis, the US Census dataset (comprising 2.5 million records with 69 features) presented substantial computational challenges. Processing a representative sample of 100,000 records (4% of the total dataset) required approximately one hour of computation time on a MacBook M3 Pro with 11 cores and 18 GB RAM. Extrapolation suggests that processing the complete dataset would require substantial computational resources exceeding typical workstation capabilities, particularly regarding memory constraints. For practical implementations with datasets of this magnitude, we recommend employing: (1) stratified sampling techniques to identify discriminative features  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline  & \multirow{2}{*}{**Features**} & \multirow{2}{*}{ \begin{tabular}{l} **KNN** \\ **Accuracy** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{l} **KNN** \\ **Precision** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{l} **KNN** \\ **Recall** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{l} **KNN F1** \\ **Accuracy** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{l} **RF** \\ **Precision** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{l} **RF** \\ **Recall** \\ \end{tabular}  } & \multirow{2}{*}{\