mlsanityData to 14 Oct 2026

The autofeat Python Library for Automated Feature Engineering and Selection

arXiv 1901.07329 ↗First posted 22 Jan 2019cs.LG stat.MLcited by 73

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Abstract (arXiv)

This paper describes the autofeat Python library, which provides scikit-learn style linear regression and classification models with automated feature engineering and selection capabilities. Complex non-linear machine learning models, such as neural networks, are in practice often difficult to train and even harder to explain to non-statisticians, who require transparent analysis results as a basis for important business decisions. While linear models are efficient and intuitive, they generally provide lower prediction accuracies. Our library provides a multi-step feature engineering and selection process, where first a large pool of non-linear features is generated, from which then a small and robust set of meaningful features is selected, which improve the prediction accuracy of a linear model while retaining its interpretability.

Key facts. Cited by 73 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Jan 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 6; 2023: 8; 2024: 16; 2025: 24; 2026: 19.

73 papers.

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  1. 2023Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineeringcited by 127
  2. 2025MLE-STAR: Machine Learning Engineering Agent via Search and Targeted Refinementcited by 58
  3. 2022OpenFE: Automated Feature Generation with Expert-level Performancecited by 52
  4. 2024Optimized Feature Generation for Tabular Data via LLMs with Decision Tree Reasoningcited by 49
  5. 2025LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizerscited by 38
  6. 2022Group-wise Reinforcement Feature Generation for Optimal and Explainable Representation Space Reconstructioncited by 35
  7. 2024Evolutionary Large Language Model for Automated Feature Transformationcited by 29
  8. 2022Traceable Automatic Feature Transformation via Cascading Actor-Critic Agentscited by 26
  9. 2023Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization Perspectivecited by 24
  10. 2023Reinforcement-Enhanced Autoregressive Feature Transformation: Gradient-steered Search in Continuous Space for Postfix Expressionscited by 23
  11. 2024Unsupervised Generative Feature Transformation via Graph Contrastive Pre-training and Multi-objective Fine-tuningcited by 21
  12. 2024Automated data processing and feature engineering for deep learning and big data applications: a surveycited by 14
38 more
  1. 2024Reinforcement Feature Transformation for Polymer Property Performance Predictioncited by 13
  2. 2024Topology-aware Reinforcement Feature Space Reconstruction for Graph Datacited by 11
  3. 2025Unsupervised Feature Transformation via In-context Generation, Generator-critic LLM Agents, and Duet-play Teamingcited by 11
  4. 2024ELF-Gym: Evaluating Large Language Models Generated Features for Tabular Predictioncited by 10
  5. 2024Large Language Models Engineer Too Many Simple Features For Tabular Datacited by 10
  6. 2023Feature Interaction Aware Automated Data Representation Transformationcited by 6
  7. 2025Autonomous Data Agents: A New Opportunity for Smart Datacited by 6
  8. 2023Auto-FP: An Experimental Study of Automated Feature Preprocessing for Tabular Datacited by 5
  9. 2024Enhancing Tabular Data Optimization with a Flexible Graph-based Reinforced Exploration Strategycited by 5
  10. 2025Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysiscited by 5
  11. 2025FastFT: Accelerating Reinforced Feature Transformation via Advanced Exploration Strategiescited by 5
  12. 2025LLM-ML Teaming: Integrated Symbolic Decoding and Gradient Search for Valid and Stable Generative Feature Transformationcited by 5
  13. 2022Self-Optimizing Feature Transformationcited by 4
  14. 2023That Escalated Quickly: An ML Framework for Alert Prioritizationcited by 4
  15. 2024IIFE: Interaction Information Based Automated Feature Engineeringcited by 4
  16. 2025Distribution Shift Aware Neural Tabular Learningcited by 4
  17. 2026FAMOSE: A ReAct Approach to Automated Feature Discoverycited by 4
  18. 2024FeatAug: Automatic Feature Augmentation From One-to-Many Relationship Tablescited by 3
  19. 2024Retrieval-Augmented Feature Generation for Domain-Specific Classificationcited by 3
  20. 2024Statistical Test for Auto Feature Engineering by Selective Inferencecited by 3
  21. 2025Tabular Feature Discovery With Reasoning Type Explorationcited by 3
  22. 2025ELATE: Evolutionary Language model for Automated Time-series Engineeringcited by 3
  23. 2026Causally-Guided Automated Feature Engineering with Multi-Agent Reinforcement Learningcited by 3
  24. 2022MACFE: A Meta-learning and Causality Based Feature Engineering Frameworkcited by 2
  25. 2025Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformationcited by 2
  26. 2025Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimizationcited by 2
  27. 2025FELA: A Multi-Agent Evolutionary System for Feature Engineering of Industrial Event Log Datacited by 2
  28. 2026Hierarchical and Permutation-Invariant Feature Transformation Learning via Policy-Guided Embedding Searchcited by 2
  29. 2023Deep incremental learning models for financial temporal tabular datasets with distribution shiftscited by 1
  30. 2024Federated Automated Feature Engineeringcited by 1
  31. 2025Dual-Agent Reinforcement Learning for Automated Feature Generationcited by 1
  32. 2025GeoReg: Weight-Constrained Few-Shot Regression for Socio-Economic Estimation using LLMcited by 1
  33. 2025How Usable is Automated Feature Engineering for Tabular Data?cited by 1
  34. 2025GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancementcited by 1
  35. 2025Dataforge: Agentic Platform for Autonomous Data Engineeringcited by 1
  36. 2026NSR-Boost: A Neuro-Symbolic Residual Boosting Framework for Industrial Legacy Modelscited by 1
  37. 2026Embedding-Aware Feature Discovery: Bridging Latent Representations and Interpretable Features in Event Sequencescited by 1
  38. 2026FeatEHR-LLM: Leveraging Large Language Models for Feature Engineering in Electronic Health Recordscited by 1

50 most cited of 73 All 73 (JSON)