explanatory scope

global

Provides explanations for overall model behavior

25 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...
Mean Decrease Impurity	Algorithmic	Tree Based	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	Linear Model	Tabular	Coefficient Magnitudes assess feature influence in linear models by examining the absolute values of their coefficients....
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...
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...
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...
RuleFit	Algorithmic	Model Agnostic	Any	RuleFit is a method that creates an interpretable model by combining linear terms with decision rules. It first extracts...
Monte Carlo Dropout	Algorithmic	Neural Network	Any	Monte Carlo Dropout estimates prediction uncertainty by applying dropout (randomly setting neural network weights to...
Out-of-DIstribution detector for Neural networks	Algorithmic	Neural Network	Any	ODIN (Out-of-Distribution Detector for Neural Networks) identifies when a neural network encounters inputs significantly...
Permutation Tests	Algorithmic	Model Agnostic	Any	Permutation tests assess the statistical significance of observed results (such as model accuracy, feature importance,...
Empirical Calibration	Algorithmic	Model Agnostic	Any	Empirical calibration adjusts a model's predicted probabilities to match observed frequencies. For example, if events...
Temperature Scaling	Algorithmic	Neural Network	Any	Temperature scaling adjusts a model's confidence by applying a single parameter (temperature) to its predictions. When a...
Bootstrapping	Algorithmic	Model Agnostic	Any	Bootstrapping estimates uncertainty by repeatedly resampling the original dataset with replacement to create many new...

Rows per page

Page 1 of 2

← Back to explanatory scope|All filters