DeepBridge Robustness Analysis Report
Test Configuration
| Parameter | Value |
|---|---|
| Model Type | {{ model_type }} |
| Metric | {{ metric }} |
| Iterations | {{ iterations }} |
| Feature Subset | {{ feature_subset_display }} |
Gaussian Noise Perturbation Results
This analysis shows how the model performance changes when Gaussian noise is added to input features. The noise level represents standard deviations of the feature distribution.
Primary Model
| Noise Level | Score | Impact | Relative Drop (%) |
|---|
Quantile-based Perturbation Results
This analysis shows how the model performance changes when feature values are replaced with values sampled from different quantiles of the distribution.
Primary Model
| Perturbation Level | Score | Impact | Relative Drop (%) |
|---|
Feature Importance Analysis
This analysis shows which features have the most impact on model performance when perturbed. Features with higher scores have greater impact on model robustness.
| Feature | Importance Score | Relative Impact (%) |
|---|
Model Performance Metrics
Primary Model
| Metric | Value |
|---|
Feature Subset Comparison
This analysis compares model performance between using all features and using only the selected feature subset under different perturbation levels.
Gaussian Perturbation
| Noise Level | All Features Score | Feature Subset Score | Difference |
|---|
Quantile Perturbation
| Perturbation Level | All Features Score | Feature Subset Score | Difference |
|---|
Worst-Case Performance Analysis
This analysis shows the worst-case model performance at each perturbation level across all test iterations.
Gaussian Perturbation - Worst Case
| Noise Level | Primary Model | Feature Subset | Alternative Models |
|---|
Quantile Perturbation - Worst Case
| Perturbation Level | Primary Model | Feature Subset | Alternative Models |
|---|
Performance Distribution Boxplot
This analysis shows the performance distribution at each perturbation level using boxplots, allowing you to visualize the variability in model performance as perturbation increases.
How to interpret:
- The boxes show the interquartile range (IQR) of performance scores at each perturbation level
- The horizontal line inside each box represents the median score
- Whiskers extend to the most extreme data points within 1.5 times the IQR
- Outliers are plotted as individual points
- The first box (perturbation level 0.0) represents the baseline performance without perturbation