Constructing and Visualizing High-Quality Classifier Decision Boundary Maps
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TL;DR: An extensive evaluation considering the dimensionality-reduction (DR) projection techniques underlying decision map construction is performed, extending the visual accuracy of decision maps by proposing additional techniques to suppress errors caused by projection distortions and proposing ways to estimate and visually encode the distance-to-decision-boundary in decision maps.
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Abstract: Visualizing decision boundaries of machine learning classifiers can help in classifier design, testing and fine-tuning. Decision maps are visualization techniques that overcome the key sparsity-related limitation of scatterplots for this task. To increase the trustworthiness of decision map use, we perform an extensive evaluation considering the dimensionality-reduction (DR) projection techniques underlying decision map construction. We extend the visual accuracy of decision maps by proposing additional techniques to suppress errors caused by projection distortions. Additionally, we propose ways to estimate and visually encode the distance-to-decision-boundary in decision maps, thereby enriching the conveyed information. We demonstrate our improvements and the insights that decision maps convey on several real-world datasets.
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Citations
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UnProjection: Leveraging Inverse-Projections for Visual Analytics of High-Dimensional Data
TL;DR: In this article , a deep learning technique with the ability to approximate the inverse of any projection or mapping is presented, allowing users to interact with the learned high-dimensional representation in a visual analytics system.
EBBE-Text: Explaining Neural Networks by Exploring Text Classification Decision Boundaries.
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TL;DR: A new tool is proposed, which includes a visualization of the decision boundary and the distances of data elements to this boundary, which increases the interpretability of NN.
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