**4. Methodology**

As stated in the introduction, the RF model is an ensemble learning (parallel learning) model with high accuracy. Based on every decision tree, RF can avoid over-fitting and under-fitting problems by efficiently estimating variables on large databases in most classification problems [40]. Based on a bagging (an abbreviation for the bootstrap aggregation) strategy, the database is split into N groups to build and train multiple decision trees [41]. The grea<sup>t</sup> number of de-correlated trees can be scored by the different generated branches to balance and improve predictive performance.

### *4.1. Decision Trees*

In the decision tree algorithm, a set of splitting rules is used to partition data features into smaller spaces with similar responses by asking simple if-else questions about each feature. Every sub-space of data presents a simpler model, which is fitted to obtain predictions. This division-and-conquer technique can produce simple rules that can easily be understood and visualized by tree diagrams. In the classification trees of this study, the gini impurity and information gain criteria are computed to evaluate the possibility and performance of each tree.
