  This paper contributes to interpretable machine learning via visual knowledge
discovery in general line coordinates (GLC). The concepts of hyperblocks as
interpretable dataset units and general line coordinates are combined to create
a visual self-service machine learning model. The DSC1 and DSC2 lossless
multidimensional coordinate systems are proposed. DSC1 and DSC2 can map
multiple dataset attributes to a single two-dimensional (X, Y) Cartesian plane
using a graph construction algorithm. The hyperblock analysis was used to
determine visually appealing dataset attribute orders and to reduce line
occlusion. It is shown that hyperblocks can generalize decision tree rules and
a series of DSC1 or DSC2 plots can visualize a decision tree. The DSC1 and DSC2
plots were tested on benchmark datasets from the UCI ML repository. They
allowed for visual classification of data. Additionally, areas of hyperblock
impurity were discovered and used to establish dataset splits that highlight
the upper estimate of worst-case model accuracy to guide model selection for
high-risk decision-making. Major benefits of DSC1 and DSC2 is their highly
interpretable nature. They allow domain experts to control or establish new
machine learning models through visual pattern discovery.
