## 2 Proposed Data-Driven Approaches  
### Bootstrap Forest  
Bootstrap forest pertains to the statistical methodology called bootstrap aggregation, or bagging, which is utilised on a collection of decision trees inside an ensemble model, resulting in the averaging of several trees [11]. The training of each decision tree involves the utilisation of a bootstrap sample derived from the training data. The splitting process in each tree entails the evaluation of a randomly chosen set of predictors. Through the utilisation of this methodology, a multitude of models possessing restricted individual capacities are combined to produce a more resilient and impactful model. The final prediction for an observation is derived by computing the average of the expected values associated with that observation across all decision trees. The predictive capacity of a bootstrap forest is attained by aggregating the anticipated response values from many decision trees, which are then averaged [12]. Each individual tree in the model is nurtured by employing a bootstrap sample that is derived from the training data. A bootstrap sample is defined as a subset of observations that is selected randomly and with replacement from a given dataset. Additionally, the predictors are sampled at each split point within the decision tree. The construction of the decision tree is accomplished by utilising the recursive partitioning technique [13]. The subsequent steps outline the methodology utilised for the fitting of the training set [14]. * In order to enhance the robustness of the analysis, it is advisable to use a bootstrap sampling strategy for the selection of data for each individual tree. * The individual decision tree is constructed using a recursive partitioning algorithm. To perform each split, a set of predictors is randomly chosen, and the order of the predictors within the set is randomised. The procedure of partitioning should be iterated until a predetermined termination criterion, as delineated in the bootstrap forest configuration, is met. * The iterative procedure of executing steps one and two will persist until the desired quantity of trees specified is attained, or unless early stopping is activated. Boosting refers to the methodology of constructing a substantial tree through the iterative fitting of a series of smaller decision trees, known as layers. The tree at each stratum is composed of a limited number of splits [15]. The condition of the tree is determined by evaluating the residuals of the preceding levels, enabling each layer to rectify the fit for inadequately fitting data inherited from the preceding layers. The ultimate forecast for an observation is obtained by aggregating the individual predictions for that observation across all levels. The bootstrap forest generates an ensemble model consisting of many decision trees, which are developed in a sequential manner to form an additive model [16]. Each tier of the tree is composed of a limited number of splits. The recursive fitting process mentioned is employed to fit each layer. The sole distinction lies in the fact that the fitting process concludes upon reaching a predetermined number of divisions. The predicted value for an observation in a leaf of a given tree is determined by calculating the mean of all observationsincluded within that leaf. Figure 1 below shows the first branch of the first tree of the proposed 100 distributed bootstrap forests. The following steps delineate the methodology for the process of training:  
1. To begin, it is recommended to apply an initial layer. 2. Compute the residuals. The numbers mentioned are obtained by subtracting the expected mean of observations contained inside a leaf from their actual numerical values. 3. Employ a layer on the remaining values. 4. The construction of the additive tree is necessary. In order to get the total expected value across several layers for a given observation, it is necessary to aggregate the projected values obtained from each individual layer. 5.