Explainability
Fidelity
Accurately represents true model behaviour
22 techniques in this subcategory
22 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| 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... | ||
| 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.... | ||
| 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... | ||
| Contextual Decomposition | Algorithmic | Architecture/neural Networks/recurrent Requirements/white Box +1 | Text | Contextual Decomposition explains LSTM and RNN predictions by decomposing the final hidden state into contributions from... | ||
| 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... | ||
| 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... | ||
| 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... | ||
| 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... | ||
| 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... | ||
| Out-of-Distribution Detector for Neural Networks | Algorithmic | Architecture/neural Networks Paradigm/discriminative +3 | Any | ODIN (Out-of-Distribution Detector for Neural Networks) identifies when a neural network encounters inputs significantly... | ||
| Permutation Tests | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | Permutation tests assess the statistical significance of observed results (such as model accuracy, feature importance,... | ||
| Model Distillation | Algorithmic | Architecture/neural Networks Paradigm/parametric +3 | Any | Model distillation transfers knowledge from a large, complex model (teacher) to a smaller, more efficient model... | ||
| 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... | ||
| Prompt Sensitivity Analysis | Experimental | Architecture/neural Networks/transformer/llm Paradigm/generative +1 | Text | Prompt Sensitivity Analysis systematically evaluates how variations in input prompts affect large language model... | ||
| Causal Mediation Analysis in Language Models | Mechanistic Interpretability | Architecture/neural Networks/transformer Architecture/neural Networks/transformer/llm +3 | Text | Causal mediation analysis in language models is a mechanistic interpretability technique that systematically... |
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