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TRV-2026-0205Certified recordPeer-reviewed

Deep forest

Current deep-learning models are mostly built upon neural networks, i.e. multiple layers of parameterized differentiable non-linear modules that can be trained by backpropagation. In this paper, we explore the possibility of building deep models based on non-differentiable modules such as decision trees. After a discussion about the mystery behind deep neural networks, particularly by contrasting them with shallow neural networks and traditional machine-learning techniques such as decision trees and boosting mac…

Science · G Space — documented gain · certified 2026-07-13 · v1 · article view · machine-readable

Current reading — gain

Decision-tree ensemble approach called deep forest achieves excellent performance across different domains using the same default setting while requiring fewer hyper-parameters and automatically determining model complexity without gradient-based adjustment or backpropagation.

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Truvace Impact Record TRV-2026-0205, v1: “Deep forest.” Truvace, 2026-07-13. /record/TRV-2026-0205 (accessed at citation time). sha256 871dd38e1d7f76a4…

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