  To increase the interpretability and prediction accuracy of the Machine
Learning (ML) models, visualization of ML models is a key part of the ML
process. Decision Trees (DTs) are essential in machine learning (ML) because
they are used to understand many black box ML models including Deep Learning
models. In this research, two new methods for creation and enhancement with
complete visualizing Decision Trees as understandable models are suggested.
These methods use two versions of General Line Coordinates (GLC): Bended
Coordinates (BC) and Shifted Paired Coordinates (SPC). The Bended Coordinates
are a set of line coordinates, where each coordinate is bended in a threshold
point of the respective DT node. In SPC, each n-D point is visualized in a set
of shifted pairs of 2-D Cartesian coordinates as a directed graph. These new
methods expand and complement the capabilities of existing methods to visualize
DT models more completely. These capabilities allow us to observe and analyze:
(1) relations between attributes, (2) individual cases relative to the DT
structure, (3) data flow in the DT, (4) sensitivity of each split threshold in
the DT nodes, and (5) density of cases in parts of the n-D space. These
features are critical for DT models' performance evaluation and improvement by
domain experts and end users as they help to prevent overgeneralization and
overfitting of the models. The advantages of this methodology are illustrated
in the case studies on benchmark real-world datasets. The paper also
demonstrates how to generalize them for decision tree visualizations in
different General Line Coordinates.
