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MLxplain

The MLxplain package serves as a comprehensive framework designed to extend the capabilities of OmniXAI, a well-regarded Python library for explainable AI (XAI). By integrating MLxplain, users can seamlessly access and utilize a broad array of algorithms developed in-house alongside the extensive suite of OmniXAI's explanation methods. This integration provides a robust and versatile environment tailored for advanced XAI tasks.

Core Idea

Explainable AI (XAI) aims to make the decision-making process of machine learning models more transparent and understandable. MLxplain enhances OmniXAI’s core capabilities by providing additional algorithms that complement and expand the library’s existing methods. This combination allows users to benefit from both our proprietary solutions and OmniXAI's diverse array of explanation techniques, all within a single installation.

Package Overview

MLxplain can be summarized in the following key components:

  1. Integration with OmniXAI:
  2. MLxplain builds upon OmniXAI’s unified interface, which offers a range of explanation methods applicable to different data types and machine learning models.

  3. This integration ensures that users have access to a wide variety of XAI techniques in a consistent and unified manner.

  4. Inclusion of Proprietary Algorithms:

  5. MLxplain incorporates additional algorithms developed by our team, designed to address specific needs and enhance the explanatory power of the models.

  6. These algorithms are seamlessly integrated into the OmniXAI framework, providing a richer set of tools for model interpretability.

  7. Simplified Installation:

  8. With a single installation of MLxplain, users gain access to all integrated algorithms and their APIs.
  9. This streamlined approach simplifies the setup process and ensures that users can immediately leverage both OmniXAI and MLxplain’s capabilities.