## 2 Model Planning Procedures
### Preprocessing and Processing Procedures  
Figure 3 shows the model planning procedures that represent the flow of the dataset mentioned in the previous section, including the data preprocessing criteria, processing phase, and model evaluation. The preprocessing procedures are sequential, including normalization, regularization, and dimension reduction. The preliminary dataset enters the preprocessing phase with a data shape of 2000 row and 7 columns, excluding the output label. The dataset can be represented as \(A\), where \(A=\{D_{\sigma}\), \(P_{L}\), \(K_{F}\), \(\sigma_{EoA}\), \(\sigma_{EoD}\), \(\sigma_{AoA}\), \(\sigma_{AoD}\), L}, and L represents the outcome label that specifies whether it is Uma LoS, Uma NloS, Rma LoS, or RMa NLoS. Each row, \(A_{i}\), represents a single data point that will be normalized using a Z-score normalization method. The ML models may exhibit bad performance due to the outliers of un-normalized data [16]. The Z-score is applied on each data point \(A_{i}^{j}\), where it represents  
Figure 3: Model planning procedures: data preprocessing, data ML processing, and model evaluation. the distance of a data point to the mean divided by a standard deviation and can be denoted as [20]  
\[A_{i}^{j}=\frac{A_{i}^{j}-\overline{A^{j}}}{\sigma_{A^{j}}} \tag{8}\]  
where \(\overline{A^{j}}\) and \(\sigma_{A^{j}}\) are the average and standard deviation of each feature \(j\). Then, the correlation matrices are used to validate the inter-parameter correlation revealed before normalizing the features and after normalization. The normalized data points are then regularized to drop the unwanted features and to enhance the classification performance [15]. The LASSO operator is L1 regularization type, while the ElasticNet is a combination of both L1 and L2 regularization type. L1 stands for Least Absolute Error, and L2 stands for Least Square Errors [21]. The LASSO regression \(\hat{B}^{lasso}\) and ElasticNet regression \(\hat{B}^{elastic}\) can be denoted, respectively, as  
\[\hat{B}^{lasso}=argmin\Bigg{\{}\frac{1}{2}\sum_{i=1}^{N}\left(y_{i}-\beta_{0 }-\sum_{j=1}^{p}x_{ij}\beta_{j}\right)+\lambda\sum_{j=1}^{p}\left|\beta_{j} \right|\Bigg{\}}\]  
\[\text{subject to }\sum_{j=1}^{p}\left|\beta_{j}\right|\leq t \tag{9}\]  
\[\hat{B}^{elastic}=argmin\left\{\sum_{i}\frac{1}{2}\left|y_{i}-\beta_{0}-\sum_{ j=1}^{p}x_{ij}\beta_{j}\right|_{2}^{2}+\lambda\left(\alpha_{1}\sum_{j=1}^{p} \beta_{j}+\alpha_{2}\sum_{j=1}^{p}\beta_{j}^{2}\right)\right\}, \tag{10}\]  
where both \(\alpha_{1}\), \(\alpha_{2}\) affect the ratio of penalty (\(\alpha_{1}+\alpha_{2}=1\)), and \(\lambda\) is the weight of shrinkage. \(\lambda\) expresses the regularization penalty, where \(1\ \geq\ \lambda\ \geq 0\). \(\beta_{0}\) is a coefficient with a constant value, while \(\beta=(\beta_{0},\ \beta_{1},\beta_{2},\ldots,\beta_{N})\) is the coefficient vector; \(t\) denotes the degree of regularization. The evaluation of the regularization process is conducted using process runtime and a regularization coefficient evaluation. Here, the runtime was tested on an Intel(r) Core(tm) i3-2365M CPU (1.40 GHz) processor using the Python timing library. Once the regularization coefficient of a certain feature obtains 0, \(t\) the feature is considered as unwanted noisy data. The last preprocessing step is the dimension projection. This is used to reduce the number of predictors for the ML, which reflects on the computational complexity reduction [22]. Here, the kernel principal component analysis (k-PCA) is adopted. The kernel type in this paper is a radial basis function (RBF) and can be denoted as  
\[\text{K}(A_{a},A_{b})=exp\left(-\rho||A_{a}-A_{b}||^{2}\right), \tag{11}\]  
where \(A_{a},A_{b}\) are two different points and \(\rho\) is a hyper-parameter threshold [23]. Then, the output components are visualized as function of probability density function (PDF). After preprocessing the data, the dimension reduction output is the number of analyzed principal components that are used as ML input predictors.