## 5 Results
Figure 5 illustrates the impacts and distributions of the principal features that substantially affect efficiency prediction throughout the dataset. The features are enumerated on the \(y\)-axis, arranged by their overall significance from top to bottom. The \(x\)-axis denotes the SHAP value, reflecting its influence on the model's output. * \(Y\)-axis indicates the feature names in order of importance from top to bottom. * \(X\)-axis represents the SHAP value, which indicates the degree of change in log odds. * The color of each point on the graph represents the value of the corresponding feature, with red indicating high values and blue indicating low values. * Each point represents a row of data from the original dataset [33]. * High (pink) means the feature has a high value for that sample. * Low (blue) means the feature has a low value for that sample. As shown in Figure 5, some features, such as Fill Factor (FF) and Maximum Voltage (Vmpp), exhibit a mix of both high (pink) and low (blue) values at similar SHAP values. This indicates that both high and low values of these features can have similar impacts on the model's prediction, depending on the specific data instance and its interaction with other features. This behavior suggests a complex or non-linear relationship between these features and the PV system efficiency. The investigation indicates that elevated levels of solar irradiation dispersion often exacerbate the adverse effects on the model, while they may mitigate negative impacts in certain instances. Increased Maximum Power (Pmpp) levels enhance the model's beneficial influence and mitigate adverse impacts. Fill Factor (FF) demonstrates a comparable trend of escalating negative impacts while diminishing their intensity. Conversely, Maximum Voltage (Vmpp) values exhibit an augmentation in the adverse impact on the model while diminishing beneficial contributions. Ultimately, Parallel Resistance (Rp) appears to augment the beneficial impact on the model while mitigating adverse contributions. Figure 4: Feature importance according to SHAP values. Global explanations furnish a comprehensive comprehension of feature influences on model behavior, whereas local explanations elucidate the rationale behind a single prediction in particular circumstances. To do this, we utilized the LIME approach to elucidate individual predictions, concentrating on two separate samples from the dataset--one exhibiting low efficiency and the other high efficiency. Figures 6 and 7 depict the local interpretability outcomes for these two scenarios. Our analysis in Figure 6 shows that the model forecasts an efficiency value of 0.19 for the specified sample. The three most significant features for this prediction are: Solar irradiation spread exceeding 0.00, which adversely impacts efficiency; Maximum Power (Pmpp) surpassing 0.63, which has a positive contribution; and Fill Factor (FF) ranging from 4.10 to 6.32, which also negatively influences the outcome. In contrast, Figure 7 illustrates the local explanation for the high-efficiency sample, which has a predicted efficiency value of 0.73. The primary contributing parameters include: solar irradiation equal to or less than 0.00, maximum power (Pmpp) values either below or above 0.63, and fill factor (FF) equal to or less than 4.10. The interpretability approaches, SHAP for global explanations and LIME for local explanations, offer complementary insights into the performance and  
Figure 5: SHAP summary graph showing the contribution of each feature to the model output. Figure 6: LIME local annotations. behavior of the CatBoost regression model, thereby augmenting our comprehension of the principal factors influencing the model's predictions.