ShapDBM: Exploring Decision Boundary Maps in Shapley Space | ScienceToStartup | ScienceToStartup

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References (28)

[1]

Computing fast and accurate maps for explaining classification models

2025Yu Wang, Cristian Grosu et al.

[2]

Fundamental Limitations of Inverse Projections and Decision Maps

2024Yu Wang, Alexandru Telea

[3]

The Shapley Value in Machine Learning

Founder's Pitch

"A novel method for visualizing machine learning decision boundaries by transforming data into Shapley space, leading to more compact and interpretable decision zones."

Explainable AI•Score: 4•View PDF ↗

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2.5

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ShapDBM: Exploring Decision Boundary Maps in Shapley Space

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References (28)

Founder's Pitch

"A novel method for visualizing machine learning decision boundaries by transforming data into Shapley space, leading to more compact and interpretable decision zones."

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ShapDBM: Exploring Decision Boundary Maps in Shapley Space

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References (28)

Founder's Pitch

"A novel method for visualizing machine learning decision boundaries by transforming data into Shapley space, leading to more compact and interpretable decision zones."

Commercial Viability Breakdown

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