## 3 Results
### Second Evaluation of Trained Models  
#### 3.4.1 Suspected Symbiotic Stars  
To assess the effectiveness and demonstrate the practical application of the trained models, we conducted the classification of two sets of stars suspected to be symbiotic stars. For this task, we utilized the ANN and Random Forest models, which showed superior performance in the earlier tests. The first dataset used was obtained from the article \"A catalogue of symbiotic stars\" [28], initially consisting of 30 candidate symbiotic stars. From the original dataset, common stars used in the model training process were excluded, as well as those lacking spectral information in GDR3. The result of this careful selection process was a set of 15 stars, which were subsequently evaluated using our previously trained models, as detailed in Table 19. The Figure 9 shows the spectral representation of the stars obtained previously from the article. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Suspected Symbiotic Stars**} & \multicolumn{3}{c}{**Trained Model (Balanced Data)**} & \multicolumn{3}{c}{**Trained Model (Unbalanced Data)**} \\ \cline{2-9}  & \multicolumn{2}{c}{**ANN**} & \multicolumn{2}{c}{**RF**} & \multicolumn{2}{c}{**ANN**} & \multicolumn{2}{c}{**RF**} \\ \cline{2-9}  & **SY** & **PN** & **RG** & **Class** & **SY** & **PN** & **RG** & **Class** \\ \hline RAW 1691 & 0.9898 & 0.006 & 0.0042 & Sy & 0.8839 & 0.0414 & 0.0748 & Sy \\ \hline
[BE74] 583 & 0.9824 & 0.0148 & 0.0029 & Sy & 0.9334 & 0.0547 & 0.0119 & Sy \\ \hline StHA 55 & 0.5252 & 0.3066 & 0.1682 & PN & 0.5468 & 0.1763 & 0.2769 & Sy \\ \hline WRAY 16\(-\)51 & 0.9998 & 0.0002 & 0.0 & Sy & 0.9829 & 0.0171 & 0.0 & Sy \\ \hline NSV 05572 & 0.9845 & 0.0154 & 0.0 & Sy & 0.9787 & 0.0213 & 0.0 & Sy \\ \hline AE Cir & 0.9839 & 0.0155 & 0.0006 & PN & 0.7039 & 0.2945 & 0.0015 & PN \\ \hline V748 Cen & 0.9999 & 0.0001 & 0.0 & PN & 0.0222 & 0.9778 & 0.0 & PN \\ \hline V345 Nor & 0.983 & 0.017 & 0.0 & Sy & 0.9492 & 0.0508 & 0.0 & Sy \\ \hline V934 Her & 0.9769 & 0.003 & 0.0201 & Sy & 0.8235 & 0.0426 & 0.134 & Sy \\ \hline Hen 3\(-\)1383 & 0.9491 & 0.0486 & 0.0022 & Sy & 0.901 & 0.099 & 0.0 & Sy \\ \hline WRAY 16\(-\)294 & 0.9134 & 0.0799 & 0.0067 & Sy & 0.8504 & 0.1486 & 0.001 & Sy \\ \hline DT Ser & 0.0 & 0.9084 & 0.0916 & RG & 0.0004 & 0.1259 & 0.8737 & RG \\ \hline V618 Sgr & 0.9962 & 0.0038 & 0.0 & Sy & 0.9759 & 0.0241 & 0.0 & Sy \\ \hline V335 Vul & 0.8891 & 0.0648 & 0.0461 & PN & 0.755 & 0.2011 & 0.0439 & PN \\ \hline V627 Cas & 0.8628 & 0.0892 & 0.0481 & Sy & 0.8563 & 0.1435 & 0.0003 & Sy \\ \hline \end{tabular}
\end{table} 
Table 19: Results of the classification using ANN and Random Forest. Both algorithms were trained using both balanced and imbalanced datasets. The dataset of suspected symbiotic stars was obtained from the article “A catalogue of symbiotic stars” [28]. The ANN model’s results are expressed as probabilities with four significant digits, whereas the Random Forest model only shows the resulting class. The second dataset was obtained from the article \"A machine learning approach for identification and classification of symbiotic stars using 2 MASS and WISE\" [20]. Like the dataset obtained from the previous article, common stars used to train the models were removed, as well as those lacking spectral information in GDR3. The outcome of this selection process was a set of 17 stars, which were subsequently evaluated using the previously trained models, as detailed in Table 20. The Figure 10 shows the spectral representation of the stars obtained previously from the article. Figure 9: The figure displays the spectra obtained from the Gaia DR3 catalog that were sourced from the article “A catalogue of symbiotic stars” [28]. The use of spectral information in the training of the models allowed for capturing specific characteristics and patterns associated with symbiotic stars. Spectral properties, such as emission and absorption lines, played a crucial role in the classification of the stars.