## 3 Results
Such a model also achieved the maximum possible classification quality on both the training set and the test set, as shown in Figure 10, which presents the  
Figure 8: Relation between number of hidden units, epochs, amount of regularization, and ROC\(-\)AUC curve on the training sample. Figure 9: Confusion matrix for the MLP neural network on the test sample. example of such an application of the PCA method is the work of [81]. The data analyzed therein came from samples of building materials and rooms tested for fungal contamination using an electronic nose. The data were visualized on a PCA plane; in the case of this work, the first two principal components explained 96.5% of the variance of the original variables. In the plot, only the reference samples were distinguished by their distribution; the other samples did not group into distinct clusters. The article [23] analyzed the samples of three types of paper, on which three different types of fungi were observed. The paper samples were examined using an electronic nose. The two-dimensional graph created by the PCA method for all data apparently discriminated the samples into three clusters, corresponding to the three paper types considered. The paper also produced additional graphs using this method, which highlighted the samples relating to only one of the paper types at 75% or 100% relative humidity. An additional method used in the paper was cluster analysis, which was also applied to the sets containing only one type of paper at 75% or 100% humidity. In the PCA charts, superior results were obtained at 100% RH, while the cluster analysis in each case distinguished between the paper samples from the control sample and those in which any type of fungus was observed. In the present study, similar results to the articles considered above were observed, as the percentage of explained variance by the first two principal components allowed for visualization of the results in two-dimensional space. In addition, in this case, the samples constituting the control observations in the PCA plot were distinguished from the others by being distinct clusters. The multidimensional scaling method is used to visualize the dissimilarity between the studied objects. In the paper [82], it was employed to show the differences between bacterial communities in three different moving bed bioreactors. The method showed that the samples from each reactor were clustered together and separated from the others on a plane. Therefore, it could be concluded that the three reactors contained different bacterial communities. In this study, the result of metric multidimensional scaling was similar to that obtained using the principal component analysis method, i.e., the reference samples differed from the other types of samples. The paper [83] used the Kohonen self-organizing map to represent the classification of facilities into groups characterized by six levels of mold infestation risk to analyze the sick building syndrome. In addition, in the paper [84], the authors used self-organizing maps to build an odor control map using the data taken with an electronic nose, from environmental odor monitoring systems. A classification into three groups created using the k-means method was overlaid on the map. Further in the article, a supervised Kohonen network was also used to predict the quantitative concentration of odor. Only observations mapped to a self-organizing map are presented in this paper. The development of the aspect related to the location of clusters on the map and the application of the supervised Kohonen network will be a further subject of research. Hierarchical cluster analysis was used in [81], where the data on mold contamination of building materials and buildings were classified into four groups. This work also investigated and classified the facilities affected by sick building syndrome. The observations from clean air samples were classified into a separate cluster.