explanatory scope
global
Provides explanations for overall model behaviour
28 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... | ||
| 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.... | ||
| 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... | ||
| 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... | ||
| Factor Analysis | Algorithmic | Architecture/model Agnostic Paradigm/unsupervised +1 | Tabular | Factor analysis is a statistical technique that identifies latent variables (hidden factors) underlying observed... | ||
| 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... | ||
| 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... | ||
| 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... | ||
| 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,... | ||
| Empirical Calibration | Algorithmic | Architecture/model Agnostic Paradigm/supervised +2 | Any | Empirical calibration adjusts a model's predicted probabilities to match observed frequencies. For example, if events... | ||
| Temperature Scaling | Algorithmic | Architecture/neural Networks Paradigm/discriminative +3 | Any | Temperature scaling adjusts a model's confidence by applying a single parameter (temperature) to its predictions. When a... | ||
| Bootstrapping | Algorithmic | Architecture/model Agnostic Paradigm/supervised +2 | Any | Bootstrapping estimates uncertainty by repeatedly resampling the original dataset with replacement to create many new... |
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