# Optimized Weighted Ensemble Approach for Enhancing Gold Mineralization Prediction
## 3 Results and Discussion
### Data Preparation and Normalization  
The features of the data that are accessible, as well as the output that is intended, should serve as the basis for choosing an acceptable normalizing procedure. Since the choice of a normalizing approach may substantially influence the accuracy of the model's predictions, it is vital to examine the efficacy of various normalization strategies on the performance of the model. This evaluation should be performed as thoroughly as possible [65]. For this analysis, a composite dataset was used that had been processed to control outliers. Prior to training the ML model, the dataset was normalized, which helped to reduce noise and improve prediction accuracy. Normalization is particularly advantageous since it ensures that the statistical distribution of the data is uniform, allowing the model to predict values more accurately for each input and output [18; 66]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **R** & **R\({}^{2}\)** & **MSE** & **RMSE** \\ \hline OK & 0.32 & 0.104 & 0.40 & 0.63 \\ IK & 0.31 & 0.096 & 0.43 & 0.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Geostatistical (OK and IK) performance for predicting gold content. Figure 7: Shows the correlation coefficients for OK and IK kriging methods. Log normalization was chosen because it can make data less skewed and more regularly distributed by reducing the effect of extreme values. This could enhance the overall performance of some machine-learning models. This is consistent with [PERSON]'s conclusion [8]. Log normalization's ability to stabilize the variance of the data is helpful for models that depend on the variance being constant. In addition, log normalization can make interpreting the model's results simpler, as the transformed values may be more intuitive or relevant to the specific application. Log normalization may not be appropriate for all data types or machine learning models. The impact of log normalization on model performance should be carefully assessed, and other normalizing methods should be considered. Experiments with Random Training and Test Partitions  
In many previous studies on predicting ore grades, the available data are typically randomized before being split into training and testing subsets. For this analysis, the dataset was divided into training (70%) and testing (30%) subsets for model evaluation. It is critical to emphasize that these two subsets should exhibit comparable statistical characteristics. Data segmentation and the MPA are optimization techniques for splitting a dataset into training and testing groups [10]. After partitioning the dataset into primary segments labeled low, medium, or high based on their gold values, they were further divided into sub-segments through a visual inspection of the histogram plot. Data segmentation was employed to improve the accuracy of resource estimates by ensuring that both the training and testing subsets of the dataset were representative of the range of gold values in the deposit (see Table 7 for more details). The study aimed to identify the combination of input variables for the resource estimating model that produced the most accurate estimates using the MPA as an optimization method. The analysis, depicted in Table 7, describes the statistical analysis of the original dataset following splitting. The evaluation of how well the data partitioning approach utilized in the study worked requires that this comparison of statistics be performed. The resource estimate model may be inaccurate if the training and testing datasets have very varying gold contents. The study shows that the data partitioning approach worked and that the resource estimate model is likely accurate and dependable by showing that the training and testing datasets had similar gold contents. Hyperparameters were tuned to obtain the optimal parameters for each ML algorithm utilized. The optimization algorithm searched for the best values for each method's parameters in the ML table (Table 8).