Summary of Optimal or Greedy Decision Trees? Revisiting Their Objectives, Tuning, and Performance, by Jacobus G. M. Van Der Linden et al.
Optimal or Greedy Decision Trees? Revisiting their Objectives, Tuning, and Performance
by Jacobus G. M. van der Linden, Daniël Vos, Mathijs M. de Weerdt, Sicco Verwer, Emir Demirović
First submitted to arxiv on: 19 Sep 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 explores the use of optimal decision trees (ODTs) in machine learning. Traditionally, decision trees are trained using greedy heuristics that optimize an impurity or information metric. However, recent research has shown that ODTs can be more accurate than greedy approaches. The paper identifies three key questions related to ODTs: the objective function used in training, tuning techniques, and a comparison of optimal and greedy methods. The authors conduct an experimental evaluation using 13 different objective functions, seven tuning methods, and six datasets to answer these questions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Decision trees are a type of machine learning algorithm that helps us make predictions or classify things into categories. Right now, we’re trying out new ways to train these decision trees so they can be more accurate. We want to know what makes the best training method and how to make it work well. In this study, researchers tested different methods for training decision trees and compared them to see which one is better. They looked at 165 datasets, both real and made-up, to figure out what works best. |
Keywords
» Artificial intelligence » Machine learning » Objective function
