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) quantifies a feature's importance in tree-based models (e.g., Random Forests, Gradient... | ||
| Coefficient Magnitudes (in Linear Models) | Metric | Architecture/linear Models Paradigm/parametric +2 | Tabular | Coefficient Magnitudes assess feature influence in linear models by examining the absolute values of their coefficients.... | ||
| Saliency Maps | Algorithmic | Architecture/neural Networks Requirements/differentiable +1 | Image | Saliency maps are visual explanations for image classification models that highlight which pixels in an image most... | ||
| Gradient-weighted Class Activation Mapping | Algorithmic | Architecture/neural Networks/convolutional Requirements/architecture Specific +2 | Image | Grad-CAM creates visual heatmaps showing which regions of an image a convolutional neural network focuses on when making... | ||
| Monte Carlo Dropout | Algorithmic | Architecture/neural Networks Paradigm/probabilistic +4 | Any | Monte Carlo Dropout estimates prediction uncertainty by applying dropout (randomly setting neural network weights to... | ||
| Attention Visualisation in Transformers | Algorithmic | Architecture/neural Networks/transformer Requirements/architecture Specific +1 | Image Text | Attention Visualisation in Transformers analyses the multi-head self-attention mechanisms that enable transformers to... |
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