## 2 Method Description
### The Second Phase of the Proposed Method
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & \(F=1.5\) & \(F=3.0\) & \(F=5.0\) & \(F=10.0\) \\ \hline Alcohol & 25.66\% & 29.16\% & 26.14\% & 31.28\% \\ \hline Appendicitis & 16.30\% & 14.57\% & 15.50\% & 15.27\% \\ \hline Australian & 23.53\% & 22.27\% & 20.81\% & 21.00\% \\ \hline Balance & 15.12\% & 13.32\% & 12.68\% & 12.95\% \\ \hline Cleveland & 51.81\% & 51.41\% & 50.70\% & 50.82\% \\ \hline Circular & 4.75\% & 4.15\% & 4.52\% & 4.19\% \\ \hline Dermatology & 36.69\% & 36.48\% & 36.39\% & 36.13\% \\ \hline Hayes-Roth & 46.18\% & 35.54\% & 34.18\% & 33.54\% \\ \hline Heart & 16.68\% & 15.93\% & 15.68\% & 15.33\% \\ \hline HeartAttack & 27.39\% & 20.38\% & 19.03\% & 18.52\% \\ \hline HouseVotes & 3.80\% & 3.35\% & 3.85\% & 3.74\% \\ \hline Ionosphere & 12.92\% & 8.27\% & 7.41\% & 7.39\% \\ \hline Liverdisorder & 30.48\% & 29.27\% & 28.48\% & 27.92\% \\ \hline Lymphography & 29.89\% & 22.41\% & 21.93\% & 20.64\% \\ \hline Mammographic & 18.00\% & 17.17\% & 16.96\% & 17.21\% \\ \hline Parkinsons & 18.25\% & 17.18\% & 15.90\% & 15.35\% \\ \hline Phoneme & 17.27\% & 15.88\% & 15.90\% & 16.62\% \\ \hline Pima & 24.54\% & 24.17\% & 24.05\% & 23.59\% \\ \hline Popfailures & 7.07\% & 5.35\% & 5.01\% & 4.80\% \\ \hline Regions2 & 26.07\% & 26.02\% & 25.78\% & 25.54\% \\ \hline Saheart & 29.75\% & 28.91\% & 29.42\% & 29.64\% \\ \hline Segment & 35.81\% & 36.84\% & 38.93\% & 40.83\% \\ \hline Sonar & 24.68\% & 19.25\% & 16.98\% & 18.25\% \\ \hline Spiral & 13.28\% & 15.25\% & 17.88\% & 22.52\% \\ \hline StatHeart & 19.98\% & 19.58\% & 19.63\% & 19.52\% \\ \hline Student & 6.14\% & 6.30\% & 5.92\% & 5.11\% \\ \hline Transfusion & 25.45\% & 25.23\% & 25.19\% & 24.59\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results for the classification datasets using a series of values for parameter \(F\). Table 7 provides the absolute error values of the proposed machine learning model across various regression datasets for four different values of the parameter \(F\) (1.5, 3.0, 5.0, 10.0). The data analysis shows that the parameter \(F\) affects the model's performance differently depending on the dataset. The average errors indicate that \(F=5.0\) yields the lowest overall error (5.22), followed by \(F=3.0\), with an average of 5.25. Higher averages are observed for \(F=1.5\) (5.52) and \(F=10.0\) (5.48), suggesting that deviating from \(F=5.0\) tends to increase error in some cases. Examining the datasets, it is evident that in several cases, increasing \(F\) improves performance, reducing error rates. For example, on the \"Abalone\" dataset, the error decreases from 6.39 for \(F=1.5\) to 5.10 for \(F=10.0\). Similarly, on the \"Friedman\" dataset, the error significantly decreases from 6.59 for \(F=1.5\) to 1.45 for \(F=10.0\). On the \"Laser\" dataset, the error decreases progressively from 0.022 for \(F=1.5\) to 0.003 for \(F=10.0\). Conversely, there are datasets where the effect of \(F\) is nonlinear or increases the error rate. For instance, on the \"Housing\" dataset, the error rises from 16.75 for \(F=1.5\) to 18.70 for \(F=10.0\). On the \"MB\" dataset, there is a sharp increase in error from 0.116 for \(F=1.5\) to 5.49 for \(F=10.0\), indicating that \(F\) significantly impacts model performance for this dataset. In summary, the parameter \(F\) has varying effects on the model's performance across different datasets. While the average indicates that \(F=5.0\) is the optimal choice, precise optimization of the parameter should be dataset-specific. Additionally, extreme parameter values may lead to significant performance degradation in certain datasets, as seen in examples like \"MB\" and \"Housing\".