Summary of Deep Trees For (un)structured Data: Tractability, Performance, and Interpretability, by Dimitris Bertsimas et al.
Deep Trees for (Un)structured Data: Tractability, Performance, and Interpretability
by Dimitris Bertsimas, Lisa Everest, Jiayi Gu, Matthew Peroni, Vasiliki Stoumpou
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Generalized Soft Trees (GSTs), an extension of soft decision trees capable of processing images directly. The authors motivate this development by recent advances in tree-based machine learning techniques and first-order optimization methods. They demonstrate the advantages of GSTs, including tractability, performance, and interpretability, through a novel algorithm called DeepTree. Benchmark tests on tabular and image datasets show that GSTs outperform popular tree methods like CART, Random Forests, XGBoost, with Convolutional Trees having a significant edge in certain datasets. The paper also explores the interpretability of GSTs, finding them more interpretable than deep neural networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates Generalized Soft Trees (GSTs) to help machines learn from pictures and other unstructured data. It’s an improvement over traditional soft decision trees because it can work directly with images. The authors show that their new method is better at processing complex datasets, like MIMIC-IV, MNIST, Fashion MNIST, CIFAR-10, and Celeb-A. They also compare GSTs to other popular methods, like CART, Random Forests, XGBoost, and find that it performs well. The most exciting part might be how easy it is to understand what the machine learned. |
Keywords
» Artificial intelligence » Machine learning » Optimization » Xgboost
