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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.