Explainability
Perturbation Based
Modifies inputs to measure impact on outputs (e.g., SHAP, Permutation Importance)
10 techniques in this subcategory
10 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| SHapley Additive exPlanations | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | SHAP explains model predictions by quantifying how much each input feature contributes to the outcome. It assigns an... | ||
| Permutation Importance | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | Permutation Importance quantifies a feature's contribution to a model's performance by randomly shuffling its values and... | ||
| Sobol Indices | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | Sobol Indices quantify how much each input feature contributes to the total variance in a model's predictions through... | ||
| Local Interpretable Model-Agnostic Explanations | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | LIME (Local Interpretable Model-agnostic Explanations) explains individual predictions by approximating the complex... | ||
| Partial Dependence Plots | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | Partial Dependence Plots show how changing one or two features affects a model's predictions on average. The technique... | ||
| Occlusion Sensitivity | Algorithmic | Architecture/model Agnostic Requirements/black Box | Image | Occlusion sensitivity tests which parts of the input are important by occluding (masking or removing) them and seeing... | ||
| Contrastive Explanation Method | Algorithmic | Architecture/neural Networks Paradigm/discriminative +4 | Any | The Contrastive Explanation Method (CEM) explains model decisions by generating contrastive examples that reveal what... | ||
| Out-of-Distribution Detector for Neural Networks | Algorithmic | Architecture/neural Networks Paradigm/discriminative +3 | Any | ODIN (Out-of-Distribution Detector for Neural Networks) identifies when a neural network encounters inputs significantly... | ||
| Permutation Tests | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | Permutation tests assess the statistical significance of observed results (such as model accuracy, feature importance,... | ||
| Prompt Sensitivity Analysis | Experimental | Architecture/neural Networks/transformer/llm Paradigm/generative +1 | Text | Prompt Sensitivity Analysis systematically evaluates how variations in input prompts affect large language model... |
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