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Amazon.com, Inc.
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Amazon.com, Inc.
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References (44)
[3]
LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers
Founder's Pitch
"FAMOSE automates feature engineering using ReAct agents for enhanced machine learning model performance on tabular data."
Commercial Viability Breakdown
0-10 scaleHigh Potential
1/4 signals
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4/4 signals
Series A Potential
2/4 signals
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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.