Summary of Theoretical and Empirical Advances in Forest Pruning, by Albert Dorador
Theoretical and Empirical Advances in Forest Pruning
by Albert Dorador
First submitted to arxiv on: 10 Jan 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Optimization and Control (math.OC)
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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 the intersection of accuracy and interpretability in machine learning models, specifically focusing on regression forests. The authors revisit forest pruning, an approach that aims to combine the strengths of both regression forests and trees. Building upon theoretical foundations of random forest theory, they prove the asymptotic advantage of Lasso-pruned forests over their unpruned counterparts under certain assumptions. Additionally, they derive high-probability finite-sample generalization bounds for pruned regression forests using various methods, which they validate through simulation experiments. The authors then test the accuracy of pruned regression forests against their unpruned counterparts on 19 datasets and find that in most scenarios, there is at least one forest-pruning method that achieves equal or better accuracy than the original full forest, while reducing the size of the forest by a significant margin. This reduction can lead to a single, interpretable tree model, offering a level of transparency superior to that of the original regression forest. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making machine learning models more understandable. Right now, some models are very good at predicting things, but we don’t know how they’re doing it. The authors want to change this by taking a powerful model called a regression forest and “pruning” it to make it smaller and easier to understand. They show that this pruned model is just as good as the original one in many cases, but with much fewer trees. This means we can get insights from the model about how it’s making predictions, which is important for many applications. |
Keywords
* Artificial intelligence * Generalization * Machine learning * Probability * Pruning * Random forest * Regression
