  Interpretability is becoming an active research topic as machine learning
(ML) models are more widely used to make critical decisions. Tabular data is
one of the most commonly used modes of data in diverse applications such as
healthcare and finance. Much of the existing interpretability methods used for
tabular data only report feature-importance scores -- either locally (per
example) or globally (per model) -- but they do not provide interpretation or
visualization of how the features interact. We address this limitation by
introducing Feature Vectors, a new global interpretability method designed for
tabular datasets. In addition to providing feature-importance, Feature Vectors
discovers the inherent semantic relationship among features via an intuitive
feature visualization technique. Our systematic experiments demonstrate the
empirical utility of this new method by applying it to several real-world
datasets. We further provide an easy-to-use Python package for Feature Vectors.
