aceDeep 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…
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.
Evidence
- Peer-reviewedWater Resources Research2018-08-30
- Peer-reviewedJournal of Network and Computer Applications2021-07-09
- Peer-reviewedNational Science Review2018-10-08
- Peer-reviewedInternational Journal of Mining Science and Technology2021-09-13
- Peer-reviewedSystems2024-01-24
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Truvace Impact Record TRV-2026-0205, v5: “Deep forest.” Truvace, 2026-09-26. /record/TRV-2026-0205 (accessed at citation time). sha256 82c88bdae748e3c3…
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