Robustness Analysis Summary

This analysis evaluates how the model's performance changes when input features are perturbed. A robust model maintains consistent performance despite noise or variations in the input data.

Key Findings

Overall Robustness
{% if robustness_score >= 0.9 %} Excellent ({{ (robustness_score * 100)|int }}%) {% elif robustness_score >= 0.8 %} Good ({{ (robustness_score * 100)|int }}%) {% elif robustness_score >= 0.7 %} Moderate ({{ (robustness_score * 100)|int }}%) {% elif robustness_score >= 0.6 %} Fair ({{ (robustness_score * 100)|int }}%) {% else %} Limited ({{ (robustness_score * 100)|int }}%) {% endif %}
Performance Impact
{% set avg_impact = (raw_impact + quantile_impact) / 2 %} {% if avg_impact < 0.1 %} Minimal ({{ (avg_impact * 100)|round(1) }}%) {% elif avg_impact < 0.2 %} Low ({{ (avg_impact * 100)|round(1) }}%) {% elif avg_impact < 0.3 %} Moderate ({{ (avg_impact * 100)|round(1) }}%) {% elif avg_impact < 0.4 %} Significant ({{ (avg_impact * 100)|round(1) }}%) {% else %} High ({{ (avg_impact * 100)|round(1) }}%) {% endif %}
{% if feature_subset %}
Feature Selection
{{ feature_subset|length }} features selected
{% endif %}

Recommendations