applicable models

agnostic

Techniques that work with any type of machine learning model

63 techniques

		Models	Data Types	Description
SHapley Additive exPlanations	Algorithmic	Model Agnostic	Any	SHAP explains model predictions by quantifying how much each input feature contributes to the outcome. It assigns an...
Permutation Importance	Algorithmic	Model Agnostic	Any	Permutation Importance quantifies a feature's contribution to a model's performance by randomly shuffling its values and...
Sobol Indices	Algorithmic	Model Agnostic	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	Model Agnostic	Any	LIME (Local Interpretable Model-agnostic Explanations) explains individual predictions by approximating the complex...
Ridge Regression Surrogates	Algorithmic	Model Agnostic	Any	This technique approximates a complex model by training a ridge regression (a linear model with L2 regularization) on...
Partial Dependence Plots	Algorithmic	Model Agnostic	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	Model Agnostic	Any	ICE plots display the predicted output for individual instances as a function of a feature, with all other features held...
Occlusion Sensitivity	Algorithmic	Model Agnostic	Image	Occlusion sensitivity tests which parts of the input are important by occluding (masking or removing) them and seeing...
Factor Analysis	Algorithmic	Model Agnostic	Tabular	Factor analysis is a statistical technique that identifies latent variables (hidden factors) underlying observed...
Principal Component Analysis	Algorithmic	Model Agnostic	Any	Principal Component Analysis transforms high-dimensional data into a lower-dimensional representation by finding the...
t-SNE	Visualization	Model Agnostic	Any	t-SNE (t-Distributed Stochastic Neighbour Embedding) is a non-linear dimensionality reduction technique that creates 2D...
UMAP	Visualization	Model Agnostic	Any	UMAP (Uniform Manifold Approximation and Projection) is a non-linear dimensionality reduction technique that creates 2D...
Prototype and Criticism Models	Algorithmic	Model Agnostic	Any	Prototype and Criticism Models provide data understanding by identifying two complementary sets of examples: prototypes...
Influence Functions	Algorithmic	Model Agnostic	Any	Influence functions quantify how much each training example influenced a model's predictions by computing the change in...
Contrastive Explanation Method	Algorithmic	Model Agnostic	Any	The Contrastive Explanation Method (CEM) explains model decisions by generating contrastive examples that reveal what...
ANCHOR	Algorithmic	Model Agnostic	Any	ANCHOR generates high-precision if-then rules that explain individual predictions by identifying the minimal set of...
RuleFit	Algorithmic	Model Agnostic	Any	RuleFit is a method that creates an interpretable model by combining linear terms with decision rules. It first extracts...
Permutation Tests	Algorithmic	Model Agnostic	Any	Permutation tests assess the statistical significance of observed results (such as model accuracy, feature importance,...
Demographic Parity Assessment	Algorithmic	Model Agnostic	Any	Demographic Parity Assessment evaluates whether a model produces equal positive prediction rates across different...
Counterfactual Fairness Assessment	Algorithmic	Model Agnostic	Any	Counterfactual Fairness Assessment evaluates whether a model's predictions would remain unchanged if an individual's...

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