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Talent Scout

K

Keith Burghardt

Amazon.com, Inc.

J

Jienan Liu

Amazon.com, Inc.

S

Sadman Sakib

Amazon.com, Inc.

Y

Yuning Hao

Amazon.com, Inc.

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References (44)

[1]
Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)
2025Liwei Jiang, Yuanjun Chai et al.
[2]
Fastft: Accelerating Reinforced Feature Transformation via Advanced Exploration Strategies
2025Tianqi He, Xiaohan Huang et al.
[3]
LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers
2025Nikhil Abhyankar, Parshin Shojaee et al.
[4]
An LLM-Based Approach for Insight Generation in Data Analysis
2025Alberto Sánchez, Alaa Boukhary et al.
[5]
ELF-Gym: Evaluating Large Language Models Generated Features for Tabular Prediction
2024Yanlin Zhang, Ning Li et al.
[6]
Data Analysis in the Era of Generative AI
2024J. Inala, Chenglong Wang et al.
[7]
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data
2024Andrej Tschalzev, Sascha Marton et al.
[8]
Optimized Feature Generation for Tabular Data via LLMs with Decision Tree Reasoning
2024Jaehyun Nam, Kyuyoung Kim et al.
[9]
Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning
2024Sungwon Han, Jinsung Yoon et al.
[10]
Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models
2024Sebastian Bordt, Harsha Nori et al.
[11]
Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding - A Survey
2024Xi Fang, Weijie Xu et al.
[12]
UniPredict: Large Language Models are Universal Tabular Classifiers
2023Ruiyu Wang, Zifeng Wang et al.
[13]
Large Language Models as General Pattern Machines
2023Suvir Mirchandani, F. Xia et al.
[14]
Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering
2023Noah Hollmann, Samuel G. Müller et al.
[15]
Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation
2022Sérgio Jesus, J. Pombal et al.
[16]
OpenFE: Automated Feature Generation with Expert-level Performance
2022T. Zhang, Zheyu Zhang et al.
[17]
ReAct: Synergizing Reasoning and Acting in Language Models
2022Shunyu Yao, Jeffrey Zhao et al.
[18]
LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks
2022Tuan Dinh, Yuchen Zeng et al.
[19]
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
2020Nick Erickson, Jonas W. Mueller et al.
[20]
The autofeat Python Library for Automated Feature Engineering and Selection
2019F. Horn, R. Pack et al.

Showing 20 of 44 references

Founder's Pitch

"FAMOSE automates feature engineering using ReAct agents for enhanced machine learning model performance on tabular data."

Machine Learning ToolsScore: 6View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

1/4 signals

2.5

Quick Build

4/4 signals

10

Series A Potential

2/4 signals

5

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Why It Matters

This research is critical because it addresses the bottleneck of manual feature engineering, which is often a time-consuming process requiring substantial domain expertise. By automating this process, FAMOSE can significantly reduce the workload for data scientists and improve model outcomes.

Product Angle

To productize FAMOSE, it could be integrated into existing AutoML platforms as an add-on tool for automated feature engineering, potentially accessible via an API for seamless integration into existing workflows.

Disruption

FAMOSE could replace manual feature engineering processes within data science teams, reducing the need for domain-specific feature selection and refinement effort.

Product Opportunity

The market opportunity lies in the large enterprises and data science teams looking to optimize tabular data models. Companies in fintech, healthcare, or marketing would pay for improved predictive capabilities and reduced development time.

Use Case Idea

Develop a cloud service that integrates FAMOSE to automatically optimize feature engineering for businesses dealing with large volumes of tabular data, enhancing the predictive performance of their machine learning models.

Science

FAMOSE leverages the ReAct framework to create an agent that iteratively discovers, evaluates, and refines features. It autonomously interacts with data, generates hypotheses, and tests them through a feedback loop, resembling the trial-and-error process used by human data scientists. It utilizes a post-processing feature selection algorithm (mRMR) to finalize the feature set.

Method & Eval

FAMOSE was tested on a range of datasets for classification and regression tasks and showed improvements in ROC-AUC for classification and reductions in RMSE for regression tasks, although improvements were marginal in scope relative to existing methods.

Caveats

The method's real-world effectiveness may be limited by the degree of feature interpretability it can provide. Also, its dependency on the specific ReAct framework and the threshold of the iterative process for feature creation might not always yield the most efficient solutions.

Author Intelligence

Keith Burghardt

Amazon.com, Inc.
kaburg@amazon.com

Jienan Liu

Amazon.com, Inc.

Sadman Sakib

Amazon.com, Inc.

Yuning Hao

Amazon.com, Inc.

Bo Li

Amazon.com, Inc.