Parameter | Value |
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Model Type | {{ model_type }} |
Metric | {{ metric }} |
Iterations | {{ iterations }} |
Feature Subset | {{ feature_subset_display }} |
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.
Noise Level | Score | Impact | Relative Drop (%) |
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This analysis shows how the model performance changes when feature values are replaced with values sampled from different quantiles of the distribution.
Perturbation Level | Score | Impact | Relative Drop (%) |
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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 (%) |
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Metric | Value |
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This analysis compares model performance between using all features and using only the selected feature subset under different perturbation levels.
Noise Level | All Features Score | Feature Subset Score | Difference |
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Perturbation Level | All Features Score | Feature Subset Score | Difference |
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This analysis shows the worst-case model performance at each perturbation level across all test iterations.
Noise Level | Primary Model | Feature Subset | Alternative Models |
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Perturbation Level | Primary Model | Feature Subset | Alternative Models |
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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: