Explainability

Sparsity

Focuses on few, most important factors

8 techniques in this subcategory

8 techniques
GoalsModelsData TypesDescription
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...
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...
Prototype and Criticism Models
Algorithmic
Architecture/model Agnostic
Paradigm/supervised
+3
Any
Prototype and Criticism Models provide data understanding by identifying two complementary sets of examples: prototypes...
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...
ANCHOR
Algorithmic
Architecture/model Agnostic
Requirements/black Box
Any
ANCHOR generates high-precision if-then rules that explain individual predictions by identifying the minimal set of...
RuleFit
Algorithmic
Architecture/model Agnostic
Paradigm/supervised
+1
Any
RuleFit creates interpretable surrogate models that can explain complex black-box models or serve as interpretable...
Generalized Additive Models
Algorithmic
Architecture/linear Models/gam
Paradigm/parametric
+2
Tabular
An intrinsically interpretable modelling technique that extends linear models by allowing flexible, nonlinear...
Model Pruning
Algorithmic
Architecture/neural Networks
Paradigm/parametric
+4
Any
Model pruning systematically removes less important weights, neurons, or entire layers from neural networks to create...
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