SHAP accepts black box models for tabular data, PyTorch/Tensorflow models for image data, transformer modelsįor text data. We will continue improving this library to make it more comprehensive in the future. The following table shows the supported explanation methods and features in our library.
Unified interface to generate the explanations for their applications by only writing a few lines ofĬodes, and also a GUI dashboard for visualization for obtaining more insights about decisions. For practitioners, OmniXAI provides an easy-to-use Methods including "model-specific" and "model-agnostic" methods (such as feature-attribution explanation,Ĭounterfactual explanation, gradient-based explanation, feature visualization, etc). (traditional ML in Scikit-learn and deep learning models in PyTorch/TensorFlow), and a range of diverse explaination Supports multiple data types (tabular data, images, texts, time-series), multiple types of ML models OmniXAI includes a rich family of explanation methods integrated in a unified interface, which OmniXAI aims to be a one-stop comprehensive library that makes explainable AI easy forĭata scientists, ML researchers and practitioners who need explanation for various types of data, models andĮxplanation methods at different stages of ML process: Machine learning capabilities to address many pain points in explaining decisions made by machine learning OmniXAI (short for Omni eXplainable AI) is a Python machine-learning library for explainable AI (XAI), offering omni-way explainable AI and interpretable Subsequently, ChatGPTĪnalyzes these results and generates the corresponding explanations that provide developers withĪ clearer understanding of the rationale behind the model's predictions. Produced by SHAP and MACE to formulate the input prompt for ChatGPT. The latest version includes an experimental GPT explainer.
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