# Proposing Enhanced Feature Engineering and a Selection Model for Machine Learning Processes
## 3 Groundwork of eFES
### eFES Algorithms
It is observed that the model learned to stay in the bounds of 20% and 80% for LE and GE. Negative 50 range is shown to elaborate on potential of error swings (i.e., for invalid values). The green and purple sticks are for reference only. Figure 15: Set of experiments for observation of diverse set of features to study the model’s fitness factor: (**a**) With features in higher range of 15, we observe the consistent stability; (**b**) This considers 10 features to evaluate the fitness function for both EFE and FE. Clearly, we observe the improvement in Fitness Function; (**c**) With features in higher range of 15, we observe the consistent stability; (**d**) However, as expected, we noticed that features up to 20, the maximum range of fitness function is around 80%; (**e**) This shows the comparison of the various metrics and read the relevant value of the fitness factor for each study of the metrics as shown by distinct colors. Figures 18 and 19 shows the observation of the bias of the model for 20-experimental analysis. Correlation Factor (CF) is computed by the eFES algorithms. Figure 18 shows the error and accuracy increases during higher end of quantification range as shown. Figure 19 on the other hand shows that the model has achieved the desired correlation of Err and Accuracy function. Table 4 shows the outcome of our 10-experimental analysis test, where we tuned the model to discover the maximum possible practical measures as shown. We used 500+ iterations on several different datasets to validate the model stability with real world data with the functions we built in our proposed model. Red values are found to be in error. Further work is needed to investigate it. Figure 16: Accuracy Validation for Feature Optimization. Figure 17: Observation of the candle-stick analysis for Global (Err) error bounds. Figure 18: Poor Correlation of Err and Accuracy during high bias. ## 5 Comparative Analysis

This section provides a brief comparison of the latest techniques with the proposed model of eFES. The data sources are detailed in the Appendix A. Table 5 shows the listing of the dataset used. Table 6 lists the methods considered for the comparisons. Table 7 lists the table structure of the division for results in Tables 8-19. The Python and R packages used are detailed in the tools sub-section of the Appendix A. All the values are normalized to range between 0 and 1 for our functions' standard outcome measures. It should be noted that the data shown in Tables 8-19 are a subset of our total experimental analysis. The rest of the results are left out for our literature review and model comparison paper for future work/writings. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**DS** & **LASSO** & **RR** & **EN** & **GBM** & **LDA** & **MDA** & **JMI** & **NNMF** & **eFES** & **Winner** \\ \hline
11 & 0.662 & 0.572 & 0.723 & 0.882 & 0.704 & 0.772 & 0.663 & 0.823 & 0.801 & GBM \\
12 & 0.606 & 0.623 & 0.221 & 0.803 & 0.772 & 0.828 & 0.920 & 0.613 & 0.934 & **eFES** \\
13 & 0.512 & 0.691 & 0.378 & 0.723 & 0.834 & 0.583 & 0.612 & 0.593 & 0.860 & **eFES** \\
14 & 0.731 & 0.612 & 0.143 & 0.703 & 0.234 & 0.524 & 0.683 & 0.444 & 0.792 & **eFES** \\
15 & 0.771 & 0.745 & 0.123 & 0.856 & 0.803 & 0.890 & 0.583 & 0.798 & 0.812 & MDA \\
16 & 0.924 & 0.703 & 0.426 & 0.323 & 0.866 & 0.597 & 0.421 & 0.231 & 0.690 & LASSO \\
17 & 0.832 & 0.791 & 0.484 & 0.428 & 0.792 & 0.890 & 0.792 & 0.166 & 0.942 & **eFES** \\
18 & 0.883 & 0.723 & 0.923 & 0.573 & 0.723 & 0.748 & 0.842 & 0.772 & 0.793 & EN \\
19 & 0.811 & 0.596 & 0.573 & 0.803 & 0.436 & 0.800 & 0.723 & 0.724 & 0.942 & **eFES** \\
20 & 0.698 & 0.582 & 0.590 & 0.777 & 0.494 & 0.784 & 0.683 & 0.682 & 0.825 & **eFES** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance Evaluation (PE) = Error, ideally lower values are desired.