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

Interactive Decision Tree Creation and Enhancement with Complete Visualization for Explainable Modeling