Summary of Learning Accurate and Interpretable Decision Trees, by Maria-florina Balcan and Dravyansh Sharma
Learning accurate and interpretable decision trees
by Maria-Florina Balcan, Dravyansh Sharma
First submitted to arxiv on: 24 May 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 The proposed approaches in this work aim to design decision tree learning algorithms with repeated access to data from the same domain. This is achieved through novel parameterized classes of node splitting criteria in top-down algorithms that interpolate between entropy and Gini impurity based criteria, providing theoretical bounds on the number of samples needed for optimal performance. The study also explores sample complexity for tuning prior parameters in Bayesian decision tree learning, extending results to decision tree regression. Additionally, it investigates hyperparameter tuning in pruning classical algorithms like min-cost complexity pruning, as well as the interpretability of learned decision trees and a data-driven approach for optimizing accuracy-explainability trade-offs using decision trees. Finally, the significance of these approaches is demonstrated on real-world datasets by learning data-specific decision trees that are both more accurate and interpretable. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Decision trees are important tools in machine learning that help us make sense of complex data. This paper looks at how we can learn these decision trees better when we have lots of data from the same place. The authors come up with new ways to choose which features to split on, using ideas like entropy and Gini impurity. They also study how many samples we need to learn the best way to make predictions and how to prune the tree to make it smaller and more efficient. Another important part of this paper is making sure that the decision trees are easy to understand and interpret. The authors show that their approach works well on real-world datasets, making it a useful tool for making decisions. |
Keywords
* Artificial intelligence * Decision tree * Hyperparameter * Machine learning * Pruning * Regression
