  Machine learning (ML) tools with graphical user interfaces (GUI) are facing
demand from novice users who do not have the background of their underlying
concepts. These tools are frequently complex and pose unique challenges in
terms of interaction and comprehension by novice users. There is yet to be an
established set of usability heuristics to guide and assess GUI ML tool design.
To address this gap, in this paper, we extend Nielsen's heuristics for
evaluating GUI ML Tools through a set of empirical evaluations. To validate the
proposed heuristics, user testing was conducted by novice users on a prototype
that reflects those heuristics. Based on the results of the evaluations, our
new heuristics set improves upon existing heuristics in the context of ML
tools. It can serve as a resource for practitioners designing and evaluating
these tools.
