Visual Analytics of Multidimensional Projections for Constructing Classifier Decision Boundary Maps
Mateus Espadoto, Francisco Rodrigues, Alexandru Cristian Telea · 2019
Visualizing decision boundaries of modern machine learning classifiers can notably help in classifier design, testing, and fine-tuning. Dense maps are a very recent method that overcomes the key sparsity-related limitation of scatterplots for this task. However, the trustworthiness of dense maps heavily depends on the underlying dimensionality-reduction (DR) techniques they use. We design and perform a detailed study aimed at finding the best DR techniques to use when creating trustworthy dense maps, by studying a large collection of 28 DR algorithms, 4 classifiers, and 2 datasets from a real-world challenging classification problem. Our results show how one can pick suitable DR algorithms to create dense maps that help understanding classifier behavior.