Summary of Mixed Integer Linear Optimization Formulations For Learning Optimal Binary Classification Trees, by Brandon Alston et al.
Mixed integer linear optimization formulations for learning optimal binary classification trees
by Brandon Alston, Hamidreza Validi, Illya V. Hicks
First submitted to arxiv on: 10 Jun 2022
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Combinatorics (math.CO)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose four novel mixed integer linear optimization (MILO) formulations for designing optimal binary classification trees. Decision trees are popular in machine learning due to their interpretability, despite being outperformed by other methods in terms of accuracy. The authors’ approach seeks to balance the number of correctly classified datapoints and the number of branching vertices. They provide theoretical comparisons with existing work and conduct experiments on 13 public datasets to demonstrate scalability and the strength of a biobjective approach using Pareto frontiers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Decision trees are a type of machine learning algorithm that helps computers make decisions by sorting through data. This paper is about making these decision trees better. Instead of just looking at accuracy, which is how well it predicts things, this paper also looks at simplicity, or how easy it is to understand why the tree made a certain decision. The authors came up with four new ways to do this and tested them on lots of different datasets. They found that their approach worked well and was able to make good decisions even when dealing with very large amounts of data. |
Keywords
* Artificial intelligence * Classification * Machine learning * Optimization
