Difference between revisions of "Deep Decision Forest"
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|Keywords=deep decision forest, explainable AI | |Keywords=deep decision forest, explainable AI | ||
|TimeFrame=Fall 2025 | |TimeFrame=Fall 2025 | ||
| − | |Supervisor=Sławomir Nowaczyk & | + | |Supervisor=Sławomir Nowaczyk & Aurora Esteban Toscano |
|Level=Master | |Level=Master | ||
|Status=Open | |Status=Open | ||
Revision as of 17:04, 11 October 2025
| Title | Deep Decision Forest |
|---|---|
| Summary | Designing a deep model that uses decision trees instead of artificial neurons |
| Keywords | deep decision forest, explainable AIProperty "Keywords" has a restricted application area and cannot be used as annotation property by a user. |
| TimeFrame | Fall 2025 |
| References | |
| Prerequisites | |
| Author | |
| Supervisor | Sławomir Nowaczyk & Aurora Esteban Toscano |
| Level | Master |
| Status | Open |
The success of Deep Learning is largely attributed to the ability to create/extract "hierarchical features" from the data. This is successful using artificial neurons or perceptrons, and the backpropagation algorithm.
The price is, however, the very large size of the model, which translates into computational costs and a "black-box" nature, or lack of explainability.
This project aims to explore ways to train a deep model using a chain of decision trees, like layers in a neural network. It promises to significantly reduce model complexity and increase interpretability.
It's a continuation of a project done in 2024...
