Interpretation and diagnosis of machine learning models have gained renewed interest in recent years with breakthroughs in new approaches. We present Manifold, Uber’s in-house model-agnostic visualization tool for ML performance diagnosis and model debugging. Manifold utilizes visual analysis techniques to support interpretation, debugging, and comparison of machine learning models in a more transparent and interactive manner. We demonstrate current applications of the Manifold on the classification and regression tasks at Uber and discuss other potential machine learning use scenarios where Manifold can be applied.
