AI Explainability 360 for interpreting models
AI Explainability 360 is an open source toolkit that offers a variety of techniques for explaining and interpreting ML models. It supports both model-specific and model-agnostic approaches, as well as local and global explanations, providing users with a range of options for understanding their models. In addition, the toolkit is built on top of popular ML libraries, including scikit-learn and XGBoost, making it easy to integrate into existing pipelines.
Some of the features of AI Explainability 360 include the following:
- Model-agnostic and model-specific explainability techniques: AI Explainability 360 provides both model-agnostic and model-specific explainability techniques that can be used to understand and explain the predictions of any AI model. Model-agnostic techniques, such as LIME and SHAP, can be used to explain the predictions of any model, while model-specific techniques, such as feature importance and partial dependence...