Robustness Analysis: {{ model_name }}

This report presents the robustness analysis of the model against data perturbations.

Model: {{ model_type }}

Date: {{ timestamp }}

Robustness Score

{{ (robustness_score * 100)|round|int }}%

{% if robustness_score >= 0.8 %} Excellent resistance to perturbations {% elif robustness_score >= 0.6 %} Good resistance to perturbations {% elif robustness_score >= 0.4 %} Moderate resistance to perturbations {% else %} Needs improvement in perturbation resistance {% endif %}

Performance Metrics

Base Score {{ base_score|round(3) }}
Raw Impact {{ (raw_impact * 100)|round(2) }}%
Quantile Impact {{ (quantile_impact * 100)|round(2) }}%
Metric {{ metric }}

Model Information

Model Type {{ model_type }}
Features {{ feature_count|default('N/A') }}
Test Samples {{ test_samples|default('N/A') }}

Performance Overview

This section shows how the model's performance changes under different perturbation levels.

Model Comparison

Comparison of robustness between different models (if available).

Detailed Results

Model Comparison
Raw Perturbation
Model Base Score Robustness Score Raw Impact Quantile Impact
Perturbation Level Base Score Perturbed Score Impact Worst Score

Detailed Performance Analysis

Detailed analysis of the model's performance under different perturbation types and levels.

Detailed perturbation data

To see detailed analysis of specific perturbations, select features in your robustness test.

Performance Distribution

Distribution of model performance under different perturbation levels.

Boxplot distribution data

Run the robustness test with multiple iterations to see the distribution of performance.

Feature Importance Analysis

Analysis of how each feature contributes to model robustness.

Feature Importance Table

Feature Robustness Impact Model Importance

Model Feature Analysis

Analysis of model features and their relationship to robustness.

Model feature analysis

Run the robustness test with feature_analysis=True to see detailed feature analysis.