Hierarchy Representation of Data in Machine Learnings
Han Yegang Park Minjun Byun Duwon Park Inkyu
When there are models with clear-cut judgment results for several data
points, it is possible that most models exhibit a relationship where if they
correctly judge one target, they also correctly judge another target.
Conversely, if most models incorrectly judge one target, they may also
incorrectly judge another target. We propose a method for visualizing this
hierarchy among targets. This information is expected to be beneficial for
model improvement.