Explainability

Explanation Target

The aspect of model behavior or data that the technique reveals

5 subcategories • 37 techniques

Causal Pathways

Shows how effects propagate through the model

Data Patterns

Uncovers underlying structures and relationships in data

Decision Boundaries

Shows how the model separates different outcomes

Feature Importance

Identifies which inputs matter most for predictions

Internal Mechanisms

Reveals how the model processes information internally

Techniques

37 techniques

		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...
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...
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...
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...
Ridge Regression Surrogates	Algorithmic	Architecture/model Agnostic Requirements/black Box	Tabular	A ridge regression surrogate approximates a complex model with a linear equation fitted to the complex model's own...
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...
Individual Conditional Expectation Plots	Visualisation	Architecture/model Agnostic Requirements/black Box	Tabular	Individual Conditional Expectation (ICE) plots display the predicted output for individual instances as a function of a...
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...
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...
ANCHOR	Algorithmic	Architecture/model Agnostic Requirements/black Box	Tabular	ANCHOR explains one prediction of a black-box classifier with an if-then rule, called an anchor, such as 'age > 30 AND...
RuleFit	Algorithmic	Architecture/model Agnostic Paradigm/supervised +1	Tabular	RuleFit builds a predictive model that a person can read: a short list of if-then rules plus a few ordinary feature...

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