# Boosting Model Interpretability for Transparent ML in TBM Tunneling
## 4 Results
### Other Interpretable Models  
**Application of Integrated Gradients (IGs) in the ANN model. **  
Integrated gradients (IGs) comprise a technique used in ANNs to attribute each input feature's contribution to a model's prediction. It is a core method in Explainable AI (XAI), particularly effective for explaining complex deep learning models. Unlike standard gradient-based methods that assess output sensitivity to small input changes, IGs integrate gradients along a path from a baseline input (e.g., all-zero or blurred data) to the actual input. IGs compute feature importance by comparing the actual input to the baseline and integrating the gradients of the model output with respect to input features along this path. The result provides a comprehensive measure of each feature's contribution. Figure 14: A SHAP decision plot depicting observations converging at the expected value for the ANN model (**left**) and the XGB model (**right**). Figure 15: A SHAP beeswarm plot presenting the impact of every feature on the model’s output for the ANN model (**left**) and the XGB model (**right**). Mathematically, IGs are calculated as the product of a feature's difference from its baseline and the integral of the model output's gradient over the path from baseline to input. This method adheres to the two following principles:  
* Sensitivity: features with no effect on the prediction receive zero attribution. * Completeness: the sum of all feature attributions equals the difference between the model's output at the actual input and the baseline. IGs are widely applied to identify key predictive features, debug models, ensure fairness by analyzing sensitive attributes, and provide interpretable insights into black box models. By integrating gradients over a path, it avoids the noise and instability of single-gradient calculations, offering a smooth and robust explanation method. Figure 16 provides a comparison of IGs and SHAP's Deep Explainer, highlighting key observations. High positive attribution values: Features like PRESSURE_GI (2.02), the highest positive attribution, significantly contribute to the model's output, indicating a strong influence on predictions. Other features, such as WORKING PRESSURE_CW (1.60) and RING_PERCENTAGE (0.34), also positively impact the output, albeit to a lesser extent. Negative attribution values: features like A/B_VOLUME (\(-0.96\)) and STOP_PERCENTAGE (\(-0.69\)) have substantial negative attributions, meaning they significantly decrease the predicted output, reflecting inverse importance. Neutral or insignificant features: features such as EXCAVATING (0.02) and TO-TAL_FORCE_TC (0.04) show minimal attribution, suggesting negligible impact on the model's predictions. Mixed contributions: AD_PERCENTAGE (\(-0.10\)) has a small negative contribution, slightly reducing the output without significant impact. The model heavily relies on PRESSURE_GI, a key driver of the target variable, likely reflecting a critical operational or physical property. Negative contributors like A/B_VOLUME and STOP_PERCENTAGE may represent factors that oppose the desired outcome, warranting further investigation or mitigation. Features with negligible impact, such as EXCAVATING and TOTAL_FORCE_TC, appear less relevant to the model and dataset. **XGBoost's plot_importance. **  
The XGBoost plot_importance function visualizes feature importances using metrics such as the following:  
* Weight: the number of times a feature is used to split data across all trees. * Gain: the average information gain from splits involving the feature. * Cover: the average number of samples covered by splits using the feature. Figure 16: Application of integrated gradients (IGs) in the ANN model. From the visualization (Figure 17--right), feature importance based on gain in an XGBoost model highlights how specific features improve model accuracy. PRESSURE_GI is the most impactful, playing a critical role in performance, followed by TOTAL_GROUT_B and TORQUE_SC, which also contribute significantly but to a lesser extent.