Summary of Optimized Feature Generation For Tabular Data Via Llms with Decision Tree Reasoning, by Jaehyun Nam et al.
Optimized Feature Generation for Tabular Data via LLMs with Decision Tree Reasoning
by Jaehyun Nam, Kyuyoung Kim, Seunghyuk Oh, Jihoon Tack, Jaehyung Kim, Jinwoo Shin
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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 Medium Difficulty summary: In the realm of tabular prediction tasks, tree-based models and automated feature engineering methods often outperform deep learning approaches relying on learned representations. The proposed Optimizing Column feature generator with decision Tree reasoning (OCTree) framework leverages large language models (LLMs) to identify effective feature generation rules without pre-defining search spaces or solely relying on validation scores for feature selection. By incorporating decision trees to convey reasoning information, OCTree provides knowledge from prior experiments as feedback for iterative rule improvements. The empirical results demonstrate that OCTree consistently enhances the performance of various prediction models across diverse benchmarks, outperforming competing automated feature engineering methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about a new way to make predictions using data tables. It combines two things: tree-based models and large language models (LLMs). The LLMs help find good features without needing human input or special knowledge. The results show that this approach works well and can be used with different types of prediction tasks. |
Keywords
» Artificial intelligence » Decision tree » Deep learning » Feature engineering » Feature selection
