Tabular Feature Engineering
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LATTEArena: An Evaluation Framework for LLM-powered Tabular Feature Engineering (Extended Version)
Announce Type: new Abstract: Feature engineering remains essential for tabular data analysis, and Large Language Models (LLMs) have emerged as a promising paradigm for automating this process, giving rise to LLM-powered AuTomated Tabular feature Engineering (LATTE). However, the absence of standardized platforms prevents fair, cost-aware comparisons. Furthermore, complex methodological designs obscure the specific contributions of individual components; for example, although LFG integrates...
TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks
arXiv:2606.02384v1 Announce Type: new Abstract: Progress in tabular machine learning has largely focused on increasingly sophisticated model architectures. At the same time, feature engineering remains a critical yet underexplored component of real-world modeling pipelines that is entirely absent from modern benchmarks, which creates an unquantified evaluation gap. In this work, we introduce TabPrep, a lightweight preprocessing pipeline composed of feature generators that are carefully...
Bridging Expert Knowledge and Automated Feature Engineering via Self-Evolution
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ClustRecNet: A Novel End-to-End Deep Learning Framework for Clustering Algorithm Recommendation
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SurvPFN: Towards Foundation Models for Survival Predictions
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