data type
any
Applicable across all data types
84 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| SHapley Additive exPlanations | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | SHAP explains model predictions by quantifying how much each input feature contributes to the outcome. It assigns an... | ||
| Permutation Importance | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | Permutation Importance quantifies a feature's contribution to a model's performance by randomly shuffling its values and... | ||
| Integrated Gradients | Algorithmic | Architecture/neural Networks Paradigm/parametric +3 | Any | Integrated Gradients is an attribution technique that explains a model's prediction by quantifying the contribution of... | ||
| DeepLIFT | Algorithmic | Architecture/neural Networks Requirements/white Box +1 | Any | DeepLIFT (Deep Learning Important FeaTures) explains neural network predictions by decomposing the difference between... | ||
| Layer-wise Relevance Propagation | Algorithmic | Architecture/neural Networks Paradigm/parametric +2 | Any | Layer-wise Relevance Propagation (LRP) explains neural network predictions by working backwards through the network to... | ||
| Taylor Decomposition | Algorithmic | Architecture/neural Networks Requirements/gradient Access +2 | Any | Taylor Decomposition is a mathematical technique that explains neural network predictions by computing first-order and... | ||
| Sobol Indices | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | Sobol Indices quantify how much each input feature contributes to the total variance in a model's predictions through... | ||
| 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 | Any | This technique approximates a complex model by training a ridge regression (a linear model with L2 regularisation) on... | ||
| Partial Dependence Plots | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | Partial Dependence Plots show how changing one or two features affects a model's predictions on average. The technique... | ||
| Individual Conditional Expectation Plots | Visualization | Architecture/model Agnostic Requirements/black Box | Any | Individual Conditional Expectation (ICE) plots display the predicted output for individual instances as a function of a... | ||
| Classical Attention Analysis in Neural Networks | Algorithmic | Architecture/neural Networks/recurrent Requirements/architecture Specific +1 | Any | Classical attention mechanisms in RNNs and CNNs create alignment matrices and temporal attention patterns that show how... | ||
| Principal Component Analysis | Algorithmic | Architecture/model Agnostic Paradigm/unsupervised +1 | Any | Principal Component Analysis transforms high-dimensional data into a lower-dimensional representation by finding the... | ||
| t-SNE | Visualization | Architecture/model Agnostic Requirements/black Box | Any | t-SNE (t-Distributed Stochastic Neighbour Embedding) is a non-linear dimensionality reduction technique that creates 2D... | ||
| UMAP | Visualization | Architecture/model Agnostic Requirements/black Box | Any | UMAP (Uniform Manifold Approximation and Projection) is a non-linear dimensionality reduction technique that creates 2D... | ||
| 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... | ||
| Influence Functions | Algorithmic | Architecture/linear Models Architecture/neural Networks +6 | Any | Influence functions quantify how much each training example influenced a model's predictions by computing the change in... | ||
| 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 +2 | Any | RuleFit creates interpretable surrogate models that can explain complex black-box models or serve as interpretable... |
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