# Enhancing Neural Network Interpretability Through Deep Prior-Guided Expected Gradients
## 4 Experiments
### Evaluation Metrics  
To comprehensively assess the performance of different interpretation methods, we employ a suite of evaluation metrics, including keep positive mean (KPM), keep negative mean (KNM), keep absolute mean (KAM), remove positive mean (RPM), remove negative mean (RNM), and remove absolute mean (RAM) [38]. These metrics are designed to evaluate how effectively explanation methods prioritize positive, negative, and overall important features, providing a nuanced understanding of their interpretability.  
* KPM/KAM: These metrics measure the ability of the model to recover positive or important features, respectively. These metrics highlight the extent to which explanations emphasize feature contributions that positively influence predictions. For KPM, all features are initially masked, and then those with positive partial derivatives are gradually unmasked (while negative-derivative features remain masked). The model's output changes are plotted, and the area under this curve (AUC) is computed--a larger AUC indicates that the explanation method more accurately identifies features that enhance predictions. Similarly, KAM begins with full masking but sequentially unmasks features in descending order of their absolute importance (e.g., gradient magnitudes). The resulting AUC reflects how well the explanation ranks globally significant features, with higher values denoting better alignment with true feature importance.
* RPM/RNM: These metrics assess the impact of removing important features on model predictions. These metrics quantify how sensitive the model is to the absence of critical features, thereby evaluating the robustness of the explanation method. RPM starts with all positive-derivative features unmasked and negative ones masked, then progressively masks the positive features (in order of importance). The output curve's AUC measures the drop in predictions caused by removing these features--larger AUC values imply the explanation correctly highlights influential positive contributors. Conversely, RNM begins with negative-derivative features unmasked and positiveones masked, then masks the negative features sequentially. Here, a larger AUC signifies better detection of features that suppress predictions when removed.
* RAM: This metric provides a general measure of feature importance based on absolute values, capturing the overall contribution of features irrespective of their directionality. Starting with all features unmasked, RAM progressively masks them in descending order of absolute importance (e.g., gradient magnitudes). The model's output changes are tracked, and the AUC is derived from the resulting curve. A larger AUC indicates that the explanation method more accurately prioritizes features by their true impact, whether positive or negative.  
For each metric, the area under the curve (AUC) is computed, with larger values indicating superior performance in highlighting the most relevant features. These metrics collectively ensure a rigorous evaluation of the interpretability and effectiveness of the proposed methods.