Summary of Exploring Loss Design Techniques For Decision Tree Robustness to Label Noise, by Lukasz Sztukiewicz et al.
Exploring Loss Design Techniques For Decision Tree Robustness To Label Noise
by Lukasz Sztukiewicz, Jack Henry Good, Artur Dubrawski
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 explores ways to improve the robustness of decision trees in the face of noisy labels. Decision trees are a type of interpretable model, but they can be affected by label errors, which is a problem that has not been thoroughly addressed despite advances in deep learning. The authors investigate whether ideas from deep learning loss design can be applied to improve the performance of decision trees. They find that standard approaches like loss correction and symmetric losses are not effective and argue that alternative directions need to be explored. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about making sure computer models work well even when the information they’re using is incorrect or “noisy”. Decision trees are special kinds of models that are easy for humans to understand, but they can get fooled by bad data. The researchers looked at ways to make decision trees more reliable and found that some common methods don’t work very well. They think we need to try new approaches to fix this problem. |
Keywords
* Artificial intelligence * Deep learning
