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Algorithms for explaining machine learning models
The main features of seldonio/alibi are: Explainability and Fairness, Explainable AI Libraries, Model Interpretability, Model Interpretation, Guardrails and AI Safety.
Projects with overlapping indexed features include: interpretml/interpret — Interpret is an interpretable machine learning library and glassbox model framework. It provides toolkits for training… slundberg/shap — SHAP is a machine learning explainer that uses a game-theoretic framework to estimate the contribution of each feature… marcotcr/lime — This project is an agnostic model interpretability framework and explainability tool designed to provide local… andosa/treeinterpreter — TreeInterpreter. austinrochford/pycebox — ⬛ Python Individual Conditional Expectation Plot Toolbox. aerdem4/lofo-importance — Leave One Feature Out Importance.
Interpret is an interpretable machine learning library and glassbox model framework. It provides toolkits for training inherently transparent models and applying post-hoc explanation techniques to make machine learning predictions human-understandable. The framework distinguishes itself by integrating differential privacy into the training of interpretable models to prevent sensitive data from leaking through explanations. It also features a visualization tool for rendering interactive decision paths and model behavior. The library covers model explainability through feature importance calcu
SHAP is a machine learning explainer that uses a game-theoretic framework to estimate the contribution of each feature to a model prediction. It provides a set of tools for quantifying how individual input features push a specific output away from a baseline value. The project includes specialized explainers for different architectures, including high-speed implementations for decision trees and ensemble models, linearization algorithms for deep learning networks, and covariance integration for linear models. It also features a model-agnostic interpretability tool that uses a kernel method to
This project is an agnostic model interpretability framework and explainability tool designed to provide local interpretable explanations for individual predictions. It functions as a local surrogate model that approximates the behavior of any machine learning classifier or regression model to identify the most influential features for a specific instance. The framework is designed to be model-agnostic, meaning it can explain predictions across tabular, text, and image data regardless of the underlying architecture. It employs local linear approximations and feature importance visualization t
⬛ Python Individual Conditional Expectation Plot Toolbox