Explainability
Efficiency
Computationally efficient to generate
6 techniques in this subcategory
6 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| 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... | ||
| Coefficient Magnitudes (in Linear Models) | Metric | Architecture/linear Models Paradigm/parametric +2 | Tabular | Coefficient Magnitudes explains a linear model by reading its fitted coefficients. Each coefficient shows how much the... | ||
| Gradient Saliency | Algorithmic | Architecture/neural Networks Requirements/differentiable +2 | Image | Gradient saliency (also called vanilla gradient saliency) is the simplest gradient-based explanation for image... | ||
| Gradient-weighted Class Activation Mapping | Algorithmic | Architecture/neural Networks/convolutional Requirements/architecture Specific +3 | Image | Grad-CAM (Gradient-weighted Class Activation Mapping) draws a heatmap over an image showing which regions most raise a... | ||
| Attention Weight Analysis | Algorithmic | Architecture/neural Networks/recurrent Architecture/neural Networks/transformer +2 | Image Text | Attention weight analysis reads the weights an attention-based neural network assigns to the parts of its input, to see... | ||
| Explainer Latency Benchmarking | Testing | Architecture/model Agnostic Requirements/black Box | Any | Explainer latency benchmarking measures how long an explanation method takes to produce an explanation for a single... |
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