## 4 Results
The ML algorithms are run with the following hyperparameters: a maximal depth of 7; a maximum number of splits of 100, the \"Gini index\" as a split criterion for the DT algorithm; ridge regularization with the regularization strength (lambda) of 0.00046 and One-vs-One multi-class coding for LR; a kernel scale of 1; a box constraint level of 1 with One-vs-One multi-class coding for SVM; a number of learners (30), number of trees (20), and maximum number of splits (2173) for RF; a number of neighbors (10) and Euclidean distance metric for K-NN; 10 and 12 neurons in two hidden layers; and the ReLU activation function for a bi-layered ANN. To improve the performance of the ML models, the parameters are optimized using Bayesian optimization (BO). Fundamental principles of BO involve utilizing a surrogate model of a Gaussian process and an acquisition function to determine the subsequent \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Mode of** & **Annual** & **Finished Product** & **Use Case in** & **Compatibility of the** \\  & **Supply** & **Demand** & **Reference Number** & **Pre-Assembly** & **Material Size with the** \\ \hline MRMR weights & 0.1390 & 0.6009 & 0.0982 & 0.2930 & 0.0886 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the MRMR algorithm. point to be evaluated [80]. BO is an effective search algorithm capable of generating performance results for all values within the user-defined parameter range [81]. Table 3 demonstrates the hyperparameters of the ML models used in the BO hyperparameter optimization procedure. The performance metrics of the models for the training set can be seen in Table 4. In the context of the training set, the RF model consistently demonstrates optimal performance, both with and without parameter optimization, attaining near-perfect scores of 99.6%. The ANN model also exhibits high performance, exhibiting only a slight decline in performance relative to the RF model. The DT model exhibits lower accuracy than the other ML models. The LR and SVM models demonstrate competitive results, particularly after parameter optimization. The optimization of parameters has been demonstrated to enhance the accuracy of both DT and SVM. The accuracy for DT has increased from 89.8% to 91.27%, and for SVM, from 93.6% to 94.38%. Minimal enhancements in performance metrics are observed for the ANN with parameter optimization; however, a substantial increase in CPU time is evident, rising from 4.47 s to over 528 s.  
Heatmaps of the confusion matrices of ML models for the training set can be seen in Figure 3.  
Trained ML models are validated with the validation set (including 47 rows), which have not been used during training. The performance metrics of the ML models for the validation set can be seen in Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ML Models** & **Hyperparameter** & **Range** & \begin{tabular}{c} **Optimized** \\ **Hyperparameters** \\ \end{tabular}  \\ \hline \multirow{3}{*}{DT} & Maximum number of splits & [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 101; 102; 103; 104; 105; 106; 107; 108; 109; 100; 101; 103; 105; 107; 108; 109; 110; 111; 111; 111; 111; 111; 111; 112; 113; 114; 115; 116; 117; 118; 119; 120; 121; 122; 123; 124; 125; 126; 1273] & 16 \\  & Split criterion & \ \begin{tabular}{c} Gini’s diversity index, Twoing rule, \\ maximum deviance reduction \\ \end{tabular}  & \begin{tabular}{c} Twoing rule \\ \end{tabular}