Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models

• Published in: IEEE transactions on visualization and computer graphics Vol. 25; no. 1; pp. 364 - 373
• Main Authors: Jiawei Zhang, Yang Wang, Molino, Piero, Lezhi Li, Ebert, David S.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analytical models; Artificial intelligence; Artificial neural networks; Computational modeling; Data models; Debugging; Diagnosis; Interactive machine learning; Iterative methods; Machine learning; Manifolds; model comparison; model debugging; performance analysis; Regression analysis; Statistical analysis; Task analysis; Visual tasks; Visualization; Workflow

Interpretation and diagnosis of machine learning models have gained renewed interest in recent years with breakthroughs in new approaches. We present Manifold, a framework that utilizes visual analysis techniques to support interpretation, debugging, and comparison of machine learning models in a mo...