Interpretable interactive visual pattern discovery in lossless 3D
visualization is a promising way to advance machine learning. It enables end
users who are not data scientists to take control of the model development
process as a self-service. It is conducted in 3D General Line Coordinates (GLC)
visualization space, which preserves all n-D information in 3D. This paper
presents a system which combines three types of GLC: Shifted Paired Coordinates
(SPC), Shifted Tripled Coordinates (STC), and General Line Coordinates-Linear
(GLC-L) for interactive visual pattern discovery. A transition from 2-D
visualization to 3-D visualization allows for a more distinct visual pattern
than in 2-D and it also allows for finding the best data viewing positions,
which are not available in 2-D. It enables in-depth visual analysis of various
class-specific data subsets comprehensible for end users in the original
interpretable attributes. Controlling model overgeneralization by end users is
an additional benefit of this approach.

这篇论文研究了在无损 3D 可视化中的可解释的交互式视觉模式发现，可以帮助非数据科学家的最终用户自主进行模型的开发过程。它结合了三种类型的 GLC：SPC、STC 和 GLC-L，实现了交互式的视觉模式发现。从 2D 可视化到 3D 可视化的转变可以获得比 2D 更明显的视觉模式，同时还允许找到最佳的数据视角，这在 2D 中不可用。它可以深度分析各种具体类别的数据子集，使最终用户能理解原始可解释属性。此方法还可以通过最终用户控制模型泛化来获得额外的益处。

3D 中的通用线坐标

General Line Coordinates in 3D

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.

本研究提出了两种新方法，即 Bended Coordinates 和 Shifted Paired Coordinates，用于更完整地可视化可理解的决策树模型，并扩展和补充了现有方法的能力。这些方法可以观察和分析属性之间的关系、每个决策点的敏感性以及 n-D 空间中案例的密度等特征，这对于决策树模型的性能评估和改进至关重要。