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 (SHapley Additive exPlanations) explains one prediction by sharing out the gap between that prediction and a... | ||
| Permutation Importance | Algorithmic | Architecture/model Agnostic Requirements/black Box | Tabular | Permutation Importance measures how much a trained model relies on each input feature. For each feature, the values in a... | ||
| Sobol Indices | Algorithmic | Architecture/model Agnostic Requirements/black Box | Tabular | Sobol indices show how much of the variation in a model's output can be traced to each of its inputs. The analyst sets a... | ||
| 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 | Visualisation | Architecture/model Agnostic Requirements/black Box | Tabular | 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 shows which parts of an input a model's prediction is sensitive to. A region of the input, such as... | ||
| Contrastive Explanation Method | Algorithmic | Architecture/neural Networks Paradigm/discriminative +4 | Any | The Contrastive Explanation Method (CEM) explains one prediction of a classifier, such as a neural network, by... | ||
| Permutation Tests | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | A permutation test asks how likely an observed result would be if there were no real relationship in the data. The... | ||
| GNNExplainer | Algorithmic | Architecture/neural Networks Requirements/gradient Access +2 | Graph | GNNExplainer explains one prediction of a graph neural network, a model that passes information along the edges of a... | ||
| TimeSHAP | Algorithmic | Architecture/neural Networks/recurrent Requirements/black Box | Time-series | TimeSHAP explains one prediction of a model that reads a sequence of events, such as a recurrent neural network that... |
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