DeepBridge Robustness Analysis Report

Model: {{ model_name }}
Generated on: {{ timestamp }}
Robustness Summary
Overall Robustness Score
{{ robustness_score }}
Higher is better (0-1 scale)
Avg. Gaussian Impact
{{ raw_impact }}
Lower is better
Avg. Quantile Impact
{{ quantile_impact }}
Lower is better
Base Score
{{ base_score }}
{{ metric }}

Test Configuration

Parameter Value
Model Type {{ model_type }}
Metric {{ metric }}
Iterations {{ iterations }}
Feature Subset {{ feature_subset_display }}
Gaussian Perturbation
Quantile Perturbation
Feature Importance
Model Metrics
Detailed Results

Gaussian Noise Perturbation Results

Gaussian Perturbation Analysis

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

Quantile Perturbation Analysis

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 for Robustness

Feature Sensitivity 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

Model Metrics

Primary Model

Metric Value

Detailed Test Results

Run Details