expertise needed

statistics

Requires knowledge of statistical methods and analysis

49 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...
Integrated Gradients	Algorithmic	Neural Network	Any	Integrated Gradients is an attribution technique that explains a model's prediction by quantifying the contribution of...
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...
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...
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...
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,...
Demographic Parity Assessment	Algorithmic	Model Agnostic	Any	Demographic Parity Assessment evaluates whether a model produces equal positive prediction rates across different...
Sensitivity Analysis for Fairness	Algorithmic	Model Agnostic	Any	Sensitivity Analysis for Fairness systematically evaluates how model predictions change when sensitive attributes or...
Synthetic Data Generation	Algorithmic	Model Agnostic	Any	Synthetic data generation creates artificial datasets that aim to preserve the statistical properties, distributions,...
Differential Privacy	Algorithmic	Model Agnostic	Any	Differential privacy provides mathematically rigorous privacy protection by adding carefully calibrated random noise to...
Prediction Intervals	Algorithmic	Model Agnostic	Any	Prediction intervals provide a range of plausible values around a model's prediction, expressing uncertainty as 'the...
Quantile Regression	Algorithmic	Model Agnostic	Any	Quantile regression estimates specific percentiles (quantiles) of the target variable rather than just predicting the...

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