What makes classification trees comprehensible?

• In-depth survey for empirical study of the classification-tree comprehensibility.
• Objective measurements suggest the most influential parameter: the depth of leaves.
• Number of leaves is a relevant comprehensibility measure only for complex trees.
• Tree visualization style and layout significantly influence the comprehensibility.
• Proposed 2 comprehensibility measures considering semantics and structure of the tree.

Classification trees are attractive for practical applications because of their comprehensibility. However, the literature on the parameters that influence their comprehensibility and usability is scarce. This paper systematically investigates how tree structure parameters (the number of leaves, branching factor, tree depth) and visualisation properties influence the tree comprehensibility. In addition, we analyse the influence of the question depth (the depth of the deepest leaf that is required when answering a question about a classification tree), which turns out to be the most important parameter, even though it is usually overlooked. The analysis is based on empirical data that is obtained using a carefully designed survey with 98 questions answered by 69 respondents. The paper evaluates several tree-comprehensibility metrics and proposes two new metrics (the weighted sum of the depths of leaves and the weighted sum of the branching factors on the paths from the root to the leaves) that are supported by the survey results. The main advantage of the new comprehensibility metrics is that they consider the semantics of the tree in addition to the tree structure itself.