## 5 Data Preprocessing
### Results  
The performance of the classifiers is calculated by using the cross-validation technique, which involves dividing the dataset into i samples. In each iteration, one sample is used as the test set and the remaining samples as the training set. For each iteration, the test set is changed, and this process is repeated until all samples have been a test set (see Figure 11). This is performed to have a more accurate evaluation of the classifier models so that the performance does not depend on the test set. For this purpose, the Preparation Matrix (Equation (11)) is divided into five samples (i = 5) for cross-validation, as illustrated in Figure 11.  
The metrics used for cross-validation are accuracy (Equation (2)) and F1 score (Equation (8)), which indicate how well the different models classify. Figure 10: Evaluation of the parameter K. \begin{table}
\begin{tabular}{l l} \hline \hline
**Classifier** & **Main Hyperparameters Optimized with Grid Search** \\ \hline K-NN & Number of neighbors = 18, distance = Euclidean, weight = distance \\ SVM & C = 100, Kernel = Polynomial, Bias = 0, Gamma = 1 \\ DT & Criterion = entropy, maximum depth = 9 \\ RF & Number of trees = 150, criterion = entropy, maximum depth = 14 = 14 \\ ANN & Hidden layers = 3, number of neurons per hidden layer = (20, 20, 20), iterations = 100 \\ CGB & Number of trees = 100, maximum depth = 12 \\ LGB & Number of trees = 50, maximum depth = \(-\)5 \\ XGB & Number of trees = 100, criterion = entropy, maximum depth = 12 \\ RL & Solution = least squares, Epsilon = 10\({}^{-8}\) \\ NB & Default probability = 0.00005, minimum standard deviation = 0.859, and standard deviation limit = 0.499 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Main hyperparameters. In Tables 10 and 11, the results of the cross-validation are presented. The classifier with the best Acc and F1 results is CGB. However, the algorithms LGB, XGB, DT, RF, and ANN have values close to it, being the best classifiers, demonstrating that these models are suitable for identifying NTLs. SVM had an average performance, while K-NN, RL, and NB obtained low productivity. Furthermore, in Table 12, the runtime of the cross-validation process is presented. The fastest classifiers were K-NN and DT, while SVM took approximately 1 h and 24 min. Comparing with its performance (Tables 10 and 11), this model is not suitable for identifying \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Acc\_1** & **Acc\_2** & **Acc\_3** & **Acc\_4** & **Acc\_5** & **Average** \\ \hline K-NN & 0.748 & 0.761 & 0.749 & 0.756 & 0.768 & 0.756 \\ SVM & 0.814 & 0.831 & 0.811 & 0.826 & 0.834 & 0.823 \\ DT & 0.878 & 0.879 & 0.869 & 0.879 & 0.875 & 0.876 \\ RF & 0.871 & 0.865 & 0.865 & 0.872 & 0.859 & 0.866 \\ ANN & 0.879 & 0.881 & 0.876 & 0.878 & 0.877 & 0.878 \\ LGB & 0.899 & 0.894 & 0.883 & 0.895 & 0.902 & 0.894 \\ CGB & 0.904 & 0.898 & 0.886 & 0.894 & 0.903 & 0.897 \\ XGB & 0.897 & 0.87 & 0.895 & 0.899 & 0.893 & 0.891 \\ RL & 0.748 & 0.861 & 0.753 & 0.757 & 0.766 & 0.777 \\ NB & 0.551 & 0.541 & 0.528 & 0.754 & 0.511 & 0.577 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Cross-validation results—accuracy (Acc). Figure 11: Division of the matrix for cross-validation. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **F1\_1** & **F1\_2** & **F1\_3** & **F1\_4** & **F1\_5** & **Average** \\ \hline K-NN & 0.695 & 0.717 & 0.695 & 0.713 & 0.731 & 0.71 \\ SVM & 0.797 & 0.817 & 0.794 & 0.816 & 0.827 & 0.81 \\ DT & 0.876 & 0.875 & 0.859 & 0.875 & 0.869 & 0.871 \\ RF & 0.866 & 0.857 & 0.855 & 0.866 & 0.852 & 0.859 \\ ANN & 0.878 & 0.878 & 0.871 & 0.878 & 0.875 & 0.876 \\ LGB & 0.897 & 0.887 & 0.879 & 0.894 & 0.901 & 0.892 \\ CGB & 0.902 & 0.892 & 0.882 & 0.894 & 0.902 & 0.894 \\ XGB & 0.896 & 0.893 & 0.891 & 0.897 & 0.891 & 0.894 \\ RL & 0.699 & 0.823 & 0.808 & 0.718 & 0.829 & 0.775 \\ NB & 0.684 & 0.681 & 0.671 & 0.691 & 0.671 & 0.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Cross-validation results—F1 score. NTLs.