Explainability
Completeness
Attributions fully account for model output
9 techniques in this subcategory
9 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... | ||
| Mean Decrease Impurity | Algorithmic | Architecture/tree Based Paradigm/supervised +1 | Tabular | Mean Decrease Impurity (MDI) scores how much each feature contributes to a tree-based model, such as a random forest, by... | ||
| Integrated Gradients | Algorithmic | Architecture/neural Networks Architecture/neural Networks/transformer +4 | Image Tabular Text | Integrated Gradients assigns input features scores for a prediction relative to a chosen reference input, called the... | ||
| DeepLIFT | Algorithmic | Requirements/model Internals Architecture/neural Networks +2 | Any | DeepLIFT (Deep Learning Important FeaTures) explains a single prediction of a neural network by dividing the difference... | ||
| Layer-wise Relevance Propagation | Algorithmic | Requirements/model Internals Architecture/neural Networks +3 | Any | Layer-wise Relevance Propagation (LRP) explains a neural network's prediction by working backwards from the output to... | ||
| Contextual Decomposition | Algorithmic | Architecture/neural Networks/recurrent Requirements/white Box +1 | Text | Contextual Decomposition explains why an LSTM, a neural network that reads text one word at a time, made a particular... | ||
| Taylor Decomposition | Algorithmic | Requirements/model Internals Architecture/neural Networks +3 | Any | Taylor Decomposition explains one prediction of a neural network by splitting its output score into a contribution from... | ||
| 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... | ||
| 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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