## 4 Results and Discussion
### Performance with Feature Subsets  
The comparative analysis using different feature subsets (Table 6) provides critical insights into model robustness and the predictive power of specific parameter combinations. All models performed best when utilizing the full set of nine features (configuration 1*), confirming that comprehensive water quality assessment benefits from considering the full spectrum of physicochemical parameters. However, the performance degradation observed across different subset configurations varied significantly between models, revealing interesting patterns in architectural robustness. The EnhancedTabTransformer maintained the highest accuracy across most subset configurations, with its performance dropping from 95.04% with all features to 72.95% in configuration 5* (pH, hardness, chloramines, and sulfate). This relatively smaller performance decline compared to other models suggests superior feature extraction capabilities that can maintain discriminative power even with reduced input dimensionality. The TAPE architecture demonstrated remarkable resilience in configuration 5*, achieving the highest accuracy (75.93%) among all models for this specific feature subset. This indicates TAPE's effective representation learning capabilities for these particular parameters, possibly attributable to its autoencoder-based pretraining that captures intrinsic structural relationships between these specific water quality indicators. Reducing the feature set to subsets such as configuration 2* or configuration 5* led to significant performance degradation, particularly for the SAINT and FITransformer models. This highlights the importance of diverse feature representation in capturing complex relationships within water quality data. The SAINT exhibited dramatic performance deterioration across all subset configurations, with accuracy dropping from 92.31% with all features to as low as 41.44% in configuration 4* (pH, trihalomethanes, and turbidity). This suggests that SAINT's dual-attention mechanism might be optimized for higher-dimensional input spaces where complex feature interactions can be leveraged more \begin{table}
\begin{tabular}{p{85.4 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Model** & **Acc 1* (\%)** & **Acc 2* (\%)** & **Acc 3* (\%)** & **Acc 4* (\%)** & **Acc 5* (\%)** \\ \hline EnhancedTabTransformer & 95.04 & 62.53 & 68.73 & 59.55 & 72.95 \\ TAPE & 88.83 & 65.51 & 63.52 & 59.55 & 75.93 \\ SAINT & 92.31 & 43.18 & 59.31 & 41.44 & 45.91 \\ FITransformer & 82.88 & 60.79 & 58.81 & 40.45 & 54.84 \\ \hline \hline \end{tabular}  
* The water\_potability dataset consists of nine features: ‘pH’, ‘Hardness (mg/L)’, ‘TDS Solids (ppm = mg/L)’, ‘Chloramines (ppm)’, ‘Sulfate (mg/L)’, ‘Conductivity (uS/cm)’, ‘Organic_carbon (ppm)’, ‘Trihalomethanes (ug/L)’, and ‘Turbidity (NTU)’. In this study, transformer-based deep neural network models were evaluated using different combinations of these features to assess their classification performance. In the first configuration (1*), all nine features were used. The second configuration (2*) utilized a subset of four features: ‘pH’, ‘Chloramines (ppm)’, ‘Trihalomethanes (ug/L)’, and ‘Turbidity (NTU)’. The third configuration (3*) included ‘Chloramines (ppm)’, ‘Organic_carbon (ppm)’, ‘Trihalomethanes (ug/L)’, and ‘Sulfate (mg/L)’. The fourth configuration (4*) focused on three features: ‘pH’, ‘Trihalomethanes (ug/L)’, and ‘Turbidity (NTU)’. Lastly, the fifth configuration (5*) comprised ‘pH’, ‘Hardness (mg/L)’, ‘Chloramines (ppm)’, and ‘Sulfate (mg/L)’. These configurations were designed to investigate the impact of different feature subsets on model performance and to identify the most informative combinations for classifying drinkable and non-drinkable water samples.  
\end{table} 
Table 6: Comparative classification performance of top transformer-based deep neural network models on different feature subsets of the _Water_Potability_ dataset. effectively.