# Optimized Weighted Ensemble Approach for Enhancing Gold Mineralization Prediction
## 3 Results and Discussion
### Comparative Analysis  
To accurately predict the grade value of the gold resource in the Quartz Ridge region, several other model methods, including RF, K-NN, GPR, DT, and FCN, were tested against the RFKNN-MPA model developed using the same dataset and optimized features as input. To compare the results, the model's performance was analyzed by applying the same indices (R, RMSE, and R\({}^{2}\)) to the training and testing datasets. Table 9 summarizes the findings obtained from the comparison research for the training data. According to the results presented in Figure 8, the random forest (RF), RFKNN-MPA, and K-NN models exhibit significantly higher R values compared to the other models. This finding suggests a stronger linear relationship between the predicted and actual values. Among these models, the RFKNN-MPA model exhibits the highest R-value of 0.74, indicating the best linear correlation performance. The R\({}^{2}\) values for all models are relatively low, suggesting that only a small percentage of the variation in the actual values can be accounted for by the predicted values. However, the RFKNN-MPA model exhibits the highest R\({}^{2}\) value, indicating that it performs better than the other models in explaining the variation in the data. This model has an R\({}^{2}\) value of 0.54. Based on the mean square error and root mean square error values, the RFKNN-MPA model exhibits the lowest prediction error compared to the other models. The mean square error (MSE) and root mean square error (RMSE) for this model are both 0.31 and 0.59, respectively, lower than the corresponding values for the other models. This finding provides evidence that the RFKNN-MPA model outperforms the other models in accurately forecasting the amount of gold present. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Hyperparameters** \\ \hline \multirow{3}{*}{Typical configurations} & Five k-folds CV \\  & Bayesian optimization \\  & Iterations: 30 \\ \hline RF & Number of trees = 200 \\ \hline \multirow{3}{*}{K-NN} & K = 13 \\  & Metric: Euclidean distance \\ \hline \multirow{3}{*}{GPR} & Basis function: constant \\  & Isotropic Rational Quadratic kernel \\ \hline \multirow{3}{*}{DT} & leaf size: 6 \\  & Minimum leaf size: 1-9041 \\ \hline \multirow{3}{*}{FCN} & Three layers used \\  & Iterations: 1000 \\ \cline{1-1}  & Activation: ReLU \\ \hline \multirow{3}{*}{RFKNN-MPA} & Number of trees = 100 \\ \cline{1-1}  & \(\text{K}=13\) \\ \end{tabular}
\end{table} 
Table 8: Outcomes from optimizing hyperparameters to improve gold predictions on the Quartz Ridge dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metric** & **RF** & **K-NN** & **GPR** & **DT** & **FCN** & **RFKNN-MPA** \\ \hline R & 0.69 & 0.66 & 0.63 & 0.50 & 0.39 & 0.74 \\ R\({}^{2}\) & 0.47 & 0.43 & 0.40 & 0.25 & 0.15 & 0.54 \\ MSE & 0.36 & 0.47 & 0.48 & 0.51 & 0.58 & 0.31 \\ RMSE & 0.67 & 0.69 & 0.69 & 0.74 & 0.78 & 0.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance predicting gold content using the proposed method and machine learning models on the training dataset. Table 10 presents the performance of the proposed approach and machine learning models for estimating gold content on the testing dataset. The results show that the RFKNN-MPA model exhibits the highest R-value (0.77), indicating a robust linear relationship between the predicted and actual values. Another model that performs well is the K-NN model, with an R-value of 0.71. Although their R-values are lower, the other models demonstrate a linear relationship between the predicted and observed values. The models explain testing dataset variance better than the training dataset because their R\({}^{2}\) values are greater. Compared to the other models, the RFKNN-MPA model has the greatest R\({}^{2}\) value of 0.597, which indicates that this model is superior in explaining the variation seen in the data. The RFKNN-MPA model exhibits the lowest prediction error (as measured by MSE and RMSE) compared to the other models.