## 1 Introduction
[56] investigated slope stability and dilution prediction using RF, SVM, and k-nearest neighbors (KNN) classification models, analyzing a dataset with nine independent parameters from underground mines in Argentina, Brazil, and Chile, determining that the RF model outperformed the other approaches. [PERSON] et al. [57] introduced three hybrid versions of RF optimized by nature-inspired metaheuristic algorithms to predict overbreak from a dataset with seven independent variables from a highway tunnel excavation in China. The authors implemented RF models enhanced by the grey wolf optimizer(GWO-RF), the whale optimization algorithm (WOA-RF), and the tunicate swarm algorithm (TSA-RF), indicating the effectiveness of these hybrid approaches in refining RF predictions. [PERSON] et al. (2008) developed three hybrid models by integrating the Bayesian optimization (BO) algorithm with existing machine learning methods to enhance overbreak prediction using a dataset from three underground mines in China, incorporating eight independent variables. The authors explored combinations of BO with extreme gradient boosting (BO-XGB), RF (BO-RF), and SVM (BO-SVM), while also comparing them with standard XGB, RF, and SVM models. Among these, BO-XGB demonstrated the best predictive capability for the testing dataset, results that were statistically validated using the Friedman and Wilcoxon signed-rank tests. Additionally, a sensitivity analysis identified tunnel diameter as the most influential factor, contributing 90.5% to overbreak, emphasizing the need for careful control of this variable in tunneling operations. [PERSON] et al. (2010) introduced three hybrid variations of the XGB model--particle swarm optimization-XGB (PSO-XGB), whale optimization algorithm-XGB (WOA-XGB), and sparrow search algorithm-XGB (SSA-XGB)--to predict overbreak in tunnel excavations utilizing a dataset from a tunnel excavation in China, incorporating 12 independent variables. This dataset, originally provided by [PERSON] (2010), was divided into two classes: 48 samples from the upper section (crown to shoulder) and 47 samples from the lower section (shoulder to haunch). For the upper section, the SSA-XGB model outperformed the others for the testing dataset; in contrast, for the lower section, the PSO-XGB and WOA-XGB achieved better performance for the testing dataset. Additionally, the authors implemented a parameter optimization stage to enhance model performance. Expanding on this work, [PERSON] et al. (2010) investigated the integration of the SSA algorithm with seven tree-based models: classification and regression tree (CART), RF, gradient boosting decision tree (GBDT), XGB, light gradient boosting machine (LightGBM), categorical boosting (CatBoost), and Deep Forest 21 (DF21), a model derived from the grained cascade forest (gcForest). Using the same dataset as [PERSON] (2010), their analysis confirmed that SSA-XGB consistently outperformed the other models. A feature importance assessment, conducted using the Shapley additive explanation (SHAP) method, revealed the most influential parameters, while an analysis of parameter interactions provided insights into their impact on overbreak prediction. This comprehensive approach led to the development of an optimized parameter tuning method, which successfully reduced overbreak in both the upper and lower sections of the tunnel. [PERSON] et al. (2010) employed two hybrid CART models to predict dilution in a sublevel open stoping mine in Australia, characterized by a narrow-to-medium width orebody The authors used a dataset encompassing fourteen independent variables, considering both a complete dataset scenario and its subdivision into two clusters. The first model applied a stepwise selection and elimination (SSE) procedure to identify the most influential parameters before performing CART classification, while the second model utilized principal component analysis (PCA) for dimensionality reduction prior to CART implementation.