No-Code Platform for Visual Knowledge Discovering in General Line Coordinates: DV 2.0
Lincoln Huber, Boris Kovalerchuk · 2023
DV 2.0 is a no-code platform contributing to interpretable machine learning via Visual Knowledge Discovery in General Line Coordinates (GLC). GLC-Linear (GLC-L), a type of GLC, provides interactive tools to enhance the model creation process and create interpretable visualizations. DV 2.0 uses these techniques so end-users can directly involve themselves in the model creation pipeline. GLC-L visualizations are easily understood by end-users and allow for complex multidimensional relationships to be visualized. Additionally, using GLC-L allows both classification and regression models to be visualized with high degrees of accuracy. For classification, DV 2.0 also offers additional expansions of GLC-L such as GLC non-Linear (GLC-nL) and GLC Hyperblock Rules Linear (GLC-HBRL) to provide different interpretable non-linear classifiers for more complex data. DV 2.0 was tested on benchmark data from the UCI Machine Learning repository.