  This work uses visual knowledge discovery in parallel coordinates to advance
methods of interpretable machine learning. The graphic data representation in
parallel coordinates made the concepts of hypercubes and hyperblocks (HBs)
simple to understand for end users. It is suggested to use mixed and pure
hyperblocks in the proposed data classifier algorithm Hyper. It is shown that
Hyper models generalize decision trees. The algorithm is presented in several
settings and options to discover interactively or automatically overlapping or
non-overlapping hyperblocks. Additionally, the use of hyperblocks in
conjunction with language descriptions of visual patterns is demonstrated. The
benchmark data from the UCI ML repository were used to evaluate the Hyper
algorithm. It enabled the discovery of mixed and pure HBs evaluated using
10-fold cross validation. Connections among hyperblocks, dimension reduction
and visualization have been established. The capability of end users to find
and observe hyperblocks, as well as the ability of side-by-side visualizations
to make patterns evident, are among major advantages ofhyperblock technology
and the Hyper algorithm. A new method to visualize incomplete n-D data with
missing values is proposed, while the traditional parallel coordinates do not
support it. The ability of HBs to better prevent both overgeneralization and
overfitting of data over decision trees is demonstrated as another benefit of
the hyperblocks. The features of VisCanvas 2.0 software tool that implements
Hyper technology are presented.
