## 2 Methods
### Decision Trees  
Decision trees are among the most popular ML methods. There are several algorithms for developing decision tree models, including classification and regression trees (CART), chi-squared automatic interaction detection (CHAID), quick, unbiased, efficient, and statistical tree (QUEST), and C5. Among these algorithms, only CART and CHAID can be used for continuous target variables. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Technique** & **No. of Input Variables** & **No. of Datasets** \\ \hline [PERSON] et al. [75] & SVM & 2 & 80 \\ [PERSON] [76] & SVM & 12 & 100 \\ [PERSON] et al. [77] & PSO–ANN & 8 & 88 \\ [PERSON] et al. [12] & ANFIS & 2 & 109 \\ [PERSON] et al. [78] & CART & 2 & 86 \\ [PERSON] et al. [79] & ICA & 2 & 73 \\ [PERSON] et al. [80] & GEP & 6 & 102 \\ [PERSON] et al. [81] & PSO–ANFIS & 2 & 81 \\ [PERSON] et al. [82] & ANN, ANFIS & 2 & 115 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont. CART is a binary decision tree, which was developed by [PERSON] [83]. CART seeks to achieve the best feasible split. To choose the best partition, CART attempts to lessen the impurity of leaf nodes. CART uses three main indices to select the best partition, including the Gini criterion entropy and Twoing criterion. The performance of the CART is influenced by the selection of these indices. A simple presentation of the CART structure is presented in Figure 1.  
To develop the CART model, the maximum tree depth was considered as five, which showed that the sample was recursively split five times. It also indicated that there are five levels below the root node. To avoid overfitting and deal with missing values, the authors pruned the tree using the \"maximum surrogates\" technique which eliminates bottom-level splits that do not substantially improve the accuracy of the tree. In order to control how the CART tree is constructed, two stopping rules, including the (1) minimum records in the parent branch and (2) minimum records in the child branch, were considered. The value of the former parameter was set as two, and it prevented a split if the number of records in the parent node to be divided was less than two. The former parameter was set as one, and it prevented a split if the number of records in any child branch created by the split was less than one. The authors used two parameters to determine the behavior of assembly that occurs during the boosting and bagging: (1) combining rule for continuous target, and (2) the number of component models for boosting and/or bagging. The authors used the mean of the predicted values from the base models to combine the ensemble values for the continuous target of this study. For boosting and bagging, the number of base models to build was set as 10. This number may improve the CART model accuracy and stability. To fine-tune the CART tree-building process, two advanced parameters were used: (1) minimum change in impurity and (2) overfit prevention set. The former parameter was used to create a new split in the tree and was set as 0.0001. The latter parameter was used to track errors during training to prevent the CART from modeling chance variation in the data. The percentage of records was set as 30. CHAID is a widely used and non-binary decision tree, which was developed by [PERSON] [84]. CHAID creates the decision tree using several sequential combinations and splits, based on a chi-square test. \begin{table}
\begin{tabular}{c c} \hline
**Screening Rules** & **Cut-Off \({}^{*}\)** \\ \hline Maximum percentage of missing values & 70 \\ Maximum percentage of records in a single category & 90 \\ Maximum number of categories as a percentage of records & 95 \\ Minimum coefficient of variation & 0.1 \\ Minimum standards deviation & 0.0 \\ Value for important variables & 0.95 \\ \hline \end{tabular} \({}^{*}\) These are default values in IBM Modeler 18.2.1.  
\end{table} 
Table 2: Screening rules and cut-off values. Figure 1: Presentation of a simple classification and regression decision tree (CART).