## 5 Discussion  
From Figure 1b it is observed from the graph that one of the experimental input parameters i.e., Pulse off Time and Workpiece Electrical Conductivity were not contributing to the output parameter. So, in the present study, the main contributing input parameters are Gap Current, Gap Voltage, and Pulse On time on Material Removal Rate. With an increase in gap current, MRR increases regardless of whether or not the work piece is cryogenically treated. Increasing the gap current will increase the effective energy available in the machining area. A high gap current is reported to give a high current density. Furthermore, the expansion of the dielectric medium causes an increase in the impulsive force, allowing the working materials to melt and evaporate. Positive ions attack the workpiece surface more readily when the gap current increases. Consequently, the surface temperature of the workpiece rises, melting or evaporating the material. As a result, the crater size increases leading to a higher MRR. With an increase in gap current, the MRR also increases, and cryogenic treatment can further enhance the MRR value. When the gap voltage is lower, the inter electrode spark gap will be smaller. If debris accumulates at the interface between the tool and the work piece, it will cause damage. This leads to arcing, which results in the electrodes becoming corroded. Gap voltage affects \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & **Precision** & **Precision** & **Recall Value** & **Recall Value** & **Overall** \\  & **Value of \(\mathbf{{}^{\prime}0^{\prime}}\)** & **Value of \(\mathbf{{}^{\prime}1^{\prime}}\)** & **of \(\mathbf{{}^{\prime}0^{\prime}}\)** & **of \(\mathbf{{}^{\prime}1^{\prime}}\)** & **F1-Score** \\ \hline Decision Tree & 1.00 & 0.67 & 0.50 & 1.00 & 0.75 \\ Random & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\ Forest & AdaBoost & 1.00 & 0.67 & 0.50 & 1.00 & 0.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Metrics features of Classification Based Algorithms. Figure 2: (a) Variation of Loss function with respect to number of epochs; (b) Decision Tree Classification Plot; (c) Confusion Matrix plot (i) Decision Tree (ii) Random Forest (iii) Ada-Boost; (d) ACU Curve (i) Decision Tree (ii) Random Forest (iii) Ada-Boost. spark energy, which is well known. Low gap voltage reduces spark energy and results in a lower material removal rate. The MRR is typically directly related to the discharge energy, which is determined by pulse duration. Increasing pulse duration will lead to greater discharge energy. A greater number of positive ions from the plasma channel strike the work surface. As a result of the bombardment of ions, the surface temperature of the work piece increases. Hence, the rise in temperature melts work material and increases MRR. From Table 2 it is observed that the Gradient boosting algorithms yields a lower value of MSE and MAE value with a highest value of the coefficient of determination (R\({}^{2}\)) i.e., 0.93. In light of this, it can be observed that Gradient Boosting algorithm can provide more accurate results than Random Forests. They can detect intricate patterns in the data since we train them to rectify each other's error values. Random forests may not be as accurate as gradient enhancing trees. They can detect intricate patterns in the data because we train them to correct each other's mistakes. The boosted trees may overfit and begin modeling the noise if the data are noisy, though. The way they produce decisions is the other key distinction. A random forest's trees can choose their outputs in any order because each tree is independent. Then, we combine all of the individual forecasts into a single prediction, which is either the average value in regression issues or the majority class in classification questions. On the other hand, the order in which the gradient boosting trees run is set and cannot be altered. They only accept sequential assessment as a result.