{% if favicon_base64 %} {% endif %}
Resilience Analysis Report
Type: | {{ model_type|default('Unknown') }} |
Features: | {{ features|length|default(0) }} |
Primary Metric: | {{ metric|default('Accuracy')|upper }} |
Sensitive Features: | {{ sensitive_features|length|default(0) }} |
Alternative Models: | {{ report_data.alternative_models|length|default(0) }} |
Resilience Score: | {{ resilience_score|default(0)|round(4) }} |
Performance Gap: | {{ avg_performance_gap|default(0)|round(4) }} |
Distribution Shift: | {{ dist_shift|default(0)|round(4) }} |
Most Affected: | {{ most_affected_scenario }} |
Generation Time | {{ timestamp }} |
---|---|
Sensitive Features | {{ sensitive_features|join(', ') }} |
Metric | {{ metric|default('Accuracy') }} |
Report Type | Static (non-interactive) |
Model | Resilience Score | Performance Gap | Distribution Shift | {% if metrics %} {% for metric_name in metrics|sort %} {% if metric_name not in ['resilience_score', 'performance_gap', 'distribution_shift'] %}{{ metric_name|title }} | {% endif %} {% endfor %} {% endif %}
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{{ model_name }} | {{ "%.4f"|format(resilience_score) }} | {{ "%.4f"|format(avg_performance_gap) }} | {{ "%.4f"|format(dist_shift|default(0)) }} | {% if metrics %} {% for metric_name, metric_value in metrics.items() %} {% if metric_name not in ['resilience_score', 'performance_gap', 'distribution_shift'] %}{{ "%.4f"|format(metric_value|default(0)) }} | {% endif %} {% endfor %} {% endif %}
Shows the key resilience metrics for the model, including resilience score and performance gap.
{% if charts.resilience_score_chart %}Compares model performance on the worst-affected samples vs. the remaining dataset. A smaller gap indicates better resilience.
{% if charts.performance_gap_chart %}Shows the distribution shift of features between normal and stressed conditions, highlighting features with significant changes.
Compares the distributions of the most critical features between normal and stressed conditions.
Shows the most important features affecting model resilience. Features with higher importance have greater impact on model performance under stress.
Compares feature importance from the model's original training versus from resilience analysis, highlighting differences in what impacts general performance versus resilience.
Feature | Importance |
---|---|
{{ feature }} | {{ "%.4f"|format(importance) }} |
Shows the distribution of residuals (prediction errors) across different datasets, helping identify biases under stress conditions.
Shows which features are most correlated with model errors, helping identify potential areas for model improvement.
Compares different distance metrics (PSI, WD1, KS, etc.) across alpha levels, showing how distribution shift is captured by different metrics.
Shows the distribution shift of each feature as measured by different metrics, visualizing which features are most affected by different types of distribution shifts.
Compares resilience performance across different models under increasing stress levels. Models with more gradual decline are more resilient.
Shows how the performance gap changes across different alpha levels for each model. Models with smaller gaps at higher alpha levels demonstrate better resilience.
Compares the overall resilience score for each model. Higher scores indicate better performance under distribution shifts.
Compares different distance metrics (PSI, WD1, KS, etc.) across alpha levels, showing how distribution shift is captured by different metrics.