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Interpreting Deep Forest through Feature Contribution and MDI Feature Importance
Deep forest is a non-differentiable deep model which has achieved impressive empirical success across a wide variety of applications, especially on categorical/symbolic or mixed modeling tasks.
A Refined Margin Distribution Analysis for Forest Representation Learning
In this paper, we formulate the forest representation learning approach called \textsc{CasDF} as an additive model which boosts the augmented feature instead of the prediction.
Improving Generalization of Deep Neural Networks by Leveraging Margin Distribution
We utilize a convex margin distribution loss function on the deep neural networks to validate our theoretical results by optimizing the margin ratio.
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