## 4 Data--Analyses and Presentation
Data characteristics, including the original descriptive values and relevant cardinalities for cost predictors that originally had descriptive values, are presented in Table 5. For the purposes of computations and computer simulations of the ANN, the original descriptive values were ordered and pseudo-fuzzy scaled into the range <0.1, 0.9>. This scaling was performed with regard to the association of the descriptive values with the costs of construction works, following the general assumption that the greater the value taken from the range, the higher the costs. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cost Predictor** & **Min** & **Max** & \begin{tabular}{c} **Range** \\ **Max – Min** \\ \end{tabular}  & **Average** & **Median** & \ \begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular}  \\ \hline \hline
\begin{tabular}{c} Wewer pipe’s length: \(x_{2}\) (m) \\ Average depth of trench: \(x_{5}\) (m) \\ Number of manholes: \(x_{8}\) \\ Number of manholes: \(x_{9}\) \\ Number of crossings with other services: \(x_{9}\); \\ Number of crossings with other services: \(x_{9}\) \\ Works on unpaved surface: \(x_{10}\) (m) \\ Works on paved surface: \(x_{11  
Table 6 presents a sample of the dependent variable \(y\) values alongside the scaled and coded values of independent variables \(x_{j}\). The whole dataset pre-processed in such a way was utilized for further computations, computer simulations of the ANN as candidates to become the members of the ensemble, and model development. For the purpose of model development, the entire dataset, consisting of 125 records, was divided into two main subsets. The first subset included data used for supervised training, while the second was reserved for testing. The testing subset was carefully selected to ensure that the included records were representative of the entire dataset, equivalent to the data used during the supervised training of individual ANN candidates considered for membership in an ensemble. This testing subset, later referred to as \(T\), comprised 15% of the data records. Data from the testing subset were not involved in the training process, as the primary aim of the testing was to assess the knowledge generalization capabilities of both individually trained ANNs and ensembles combining selected ANNs. This approach allowed for testing with data that had not been presented to the ANNs during supervised training. The remaining 85% of the dataset was divided into two subsets for the purposes of learning, (referred to as \(L\)) and validation (referred to as \(V\)) during the supervised training of individual ANNs, which were considered candidates for ensemble membership. The division ratio into the learning, validating, and testing subsets equaled:  
\[L/V/T=70\%/15\%/15\%\]  
The division into \(L\) and \(V\) subsets was repeated five times, resulting in five folds of data available for the supervised training of candidate ANNs. The fundamental assumption was to conduct the training process five times using different learning and validating subsets. Depending on the fold, the data records belonging to the L or V subsets were rotated between the learning and validation processes and contributed to the training process accordingly.