python partial dependence plot toolbox
Las características principales de saucecat/pdpbox son: Explainable AI Libraries, Model Interpretability, Model Interpretation.
Las alternativas de código abierto para saucecat/pdpbox incluyen: marcotcr/lime — This project is an agnostic model interpretability framework and explainability tool designed to provide local… seldonio/alibi — Algorithms for explaining machine learning models. austinrochford/pycebox — ⬛ Python Individual Conditional Expectation Plot Toolbox. interpretml/interpret — Interpret is an interpretable machine learning library and glassbox model framework. It provides toolkits for training… andosa/treeinterpreter — TreeInterpreter. slundberg/shap — SHAP is a machine learning explainer that uses a game-theoretic framework to estimate the contribution of each feature…
⬛ 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
