How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
Contrastive Explanation (Foil Trees), developed at TNO/Utrecht University
The main features of marcelrobeer/contrastiveexplanation are: Model Interpretability, Model Interpretation.
Open-source alternatives to marcelrobeer/contrastiveexplanation include: marcotcr/lime — This project is an agnostic model interpretability framework and explainability tool designed to provide local… saucecat/pdpbox — python partial dependence plot toolbox. andosa/treeinterpreter — TreeInterpreter. interpretml/interpret — Interpret is an interpretable machine learning library and glassbox model framework. It provides toolkits for training… austinrochford/pycebox — ⬛ Python Individual Conditional Expectation Plot Toolbox. seldonio/alibi — Algorithms for explaining machine learning models.
⬛ Python Individual Conditional Expectation Plot Toolbox
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
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
