Explainability
Data Patterns
Uncovers underlying structures and relationships in data
11 techniques in this subcategory
11 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| 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... | ||
| Factor Analysis | Algorithmic | Architecture/model Agnostic Paradigm/unsupervised +1 | Tabular | Factor analysis looks for a small number of hidden factors that explain why a larger set of measured variables, such as... | ||
| Principal Component Analysis | Algorithmic | Architecture/model Agnostic Paradigm/unsupervised +1 | Any | Principal component analysis (PCA) summarises a dataset that has many numeric features by finding a few new axes along... | ||
| t-SNE | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | t-SNE (t-distributed Stochastic Neighbour Embedding) turns high-dimensional data into a two- or three-dimensional map... | ||
| UMAP | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | UMAP (Uniform Manifold Approximation and Projection) is a non-linear dimensionality reduction method that turns... | ||
| Prototype and Criticism Models | Algorithmic | Architecture/model Agnostic Paradigm/supervised +2 | Any | Prototype and criticism methods summarise a dataset with a small set of real examples. Prototypes are instances chosen... | ||
| Influence Functions | Algorithmic | Architecture/linear Models Architecture/neural Networks +5 | Any | Influence functions estimate which training examples most affected a model's prediction on a particular test example.... | ||
| Permutation Tests | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | A permutation test asks how likely an observed result would be if there were no real relationship in the data. The... | ||
| Generalized Additive Models | Algorithmic | Architecture/linear Models/gam Paradigm/parametric +2 | Tabular | A generalised additive model (GAM) builds its prediction by adding a baseline and one smooth curve for each input... | ||
| Neuron Activation Analysis | Algorithmic | Architecture/neural Networks Requirements/model Internals +1 | Text | Neuron activation analysis examines when individual neurons in a neural network respond strongly. The analyst runs a set... |
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