Pyreal: A Framework for Interpretable ML Explanations
Alexandra Zytek Wei-En Wang Dongyu Liu Laure Berti-Equille Kalyan Veeramachaneni
Users in many domains use machine learning (ML) predictions to help them make
decisions. Effective ML-based decision-making often requires explanations of ML
models and their predictions. While there are many algorithms that explain
models, generating explanations in a format that is comprehensible and useful
to decision-makers is a nontrivial task that can require extensive development
overhead. We developed Pyreal, a highly extensible system with a corresponding
Python implementation for generating a variety of interpretable ML
explanations. Pyreal converts data and explanations between the feature spaces
expected by the model, relevant explanation algorithms, and human users,
allowing users to generate interpretable explanations in a low-code manner. Our
studies demonstrate that Pyreal generates more useful explanations than
existing systems while remaining both easy-to-use and efficient.