Enhancing Machine Learning Systems with AUTOML: A TPOT Approach

Abstract

In the modern era of data-driven decision-making, Automated Machine Learning (AutoML) has emerged as a transformativeapproachtostreamlineandoptimizetheprocessof building machine learning models. Traditional modeldevelopment often requires deep domain knowledge, significant manual effort, and time-consuming trial-and-error processes for selecting algorithms, tuning hyperparameters, and designing data preprocessing steps. AutoML addresses these challenges by automating these tasks, making advanced machine learning accessible to both experts and non-experts. In the proposedsystem, AutoML is integrated using the TPOT (Tree-based Pipeline Optimization Tool) framework. TPOT is a genetic programming-basedAutoMLlibrary in Python that automatically explores and optimizes machine learning pipelines for regression and classification problems. It evaluates numerous combinations of data preprocessing techniques, feature selection methods, and algorithms to identify the best-performing model pipeline. This automation significantly enhances the system’s efficiency by reducing development time and eliminating manual intervention. By employing TPOT, the proposed system benefits from an intelligent search strategy that adapts to the specificcharacteristics of the dataset. It ensures that the most appropriate models are selected and fine-tuned to achieve high accuracy and generalization performance. The pipeline not only generates models with minimal human effort but also enhances scalability and adaptability to various domains and data types. Furthermore, integrating TPOT into the system allows for continuous learning and improvement as new data becomes available. This makes the system robust, dynamic, and well-suited for real-world applications where data evolves over time. The use of TPOT demonstrates the value of AutoML in reducing complexity while maintaining high performance, making it an essential component in the development of intelligent, automated, and scalablemachine learning systems. Ultimately, the adoption of AutoML through TPOT reinforces the goal of building efficient and reliable models without requiring deep machine learningexpertise.

Introduction

Overview:

The paper explores the synergistic relationship between Machine Learning (ML) and Automated Machine Learning (AutoML), focusing on improving data preprocessing—a critical but time-consuming step in ML workflows. It surveys significant research contributions and proposes an end-to-end AutoML system designed to automate data preparation, model building, and evaluation.

???? Key Themes & Objectives:

1. Symbiosis of ML and AutoML:

   • ML relies on algorithms to detect patterns and make predictions.

   • AutoML automates model selection, hyperparameter tuning, and preprocessing, making ML more accessible and efficient.

2. Data Preprocessing Focus:

   • The paper emphasizes improving preprocessing stages, especially for challenges like imbalanced data, missing values, and feature engineering.

   • Highlights tools like DataAssist, REIN, AutoCure, and reciprocal neural networks for their contributions to automated data quality improvement.

3. Literature Review:

   • A survey of relevant papers offers insights into the state-of-the-art methods for data cleaning, repair, and preparation.

   • Tools like DIGEN and REIN1 are evaluated for benchmarking ML models under varied data conditions.

???? Proposed System Architecture:

A modular AutoML system is designed around TPOT (Tree-based Pipeline Optimization Tool), utilizing genetic programming for pipeline optimization. The main components include:

1. Data Input Module: Accepts and validates uploaded datasets (CSV/Excel).

2. Preprocessing Module: Automates handling of missing values, outlier detection, feature scaling, encoding, and selection.

3. AutoML Engine (TPOT): Evolves ML pipelines using genetic programming to find optimal configurations with minimal manual effort.

4. Evaluation Module: Assesses model performance using cross-validation and metrics like accuracy, F1-score, RMSE.

5. User Interface (Streamlit): Provides a no-code, interactive front end for uploading data, visualizing results, and downloading models.

6. File Upload Module: Manages secure dataset uploads and structure verification.

7. Processed with ML Engine Module: Orchestrates pipeline configuration and optimization flow.

8. Suggest Best Model Module: Recommends and exports the best-performing pipeline based on evaluation metrics.

???? Experimental Focus Areas:

• Hyperparameter Tuning: Utilizes grid/random search to optimize model settings.

• Feature Engineering: Emphasizes generating and selecting meaningful features to improve learning performance.

• Ensemble Methods: Applies techniques like bagging, boosting, and stacking to enhance prediction accuracy and robustness.

???? Results and Insights:

• The system effectively automates preprocessing and model selection, showing significant potential to reduce development time, improve data quality, and democratize access to ML.

• Ensemble methods and hyperparameter tuning contribute heavily to predictive performance.

• Visual tools (graphs, metrics, confusion matrices) help users interpret and trust model outputs.

Conclusion

In conclusion, the integration of AutoML through TPOT in the proposed system significantly simplifies and accelerates the machine learning workflow. By automating key tasks such as data preprocessing, feature selection, algorithm selection, and hyperparameter tuning, TPOT enables users—regardless of their ML expertise—to build high-performing models efficiently. Its genetic programming-based optimization ensures that the best possible pipeline is selected for any given dataset, making the system adaptable and scalable across different domains. This automationnotonlyreducesdevelopmenttimebutalsoenhances model accuracyand generalization. Furthermore, TPOT’s ability to continuously improve with new data makes the system dynamic and suitable for real-world, evolving dataenvironments. Overall, the use of TPOT reinforces the core objective of the proposed system: to democratize machine learning by making it accessible, reliable, and efficient for both technical and non-technical users. It establishes a robust foundation for intelligent decision-making powered by automated, data-driven insights.

References

[1] K. Goyle, Q. Xie, & V. Goyle, \"DataAssist: A Machine Learning Approach to Data Cleaning and Preparation,\" eprint arXiv:2307.07119, 2023. [2] S. Juddoo, \"Investigating Data Repair steps for EHR Big Data,\" in International Conference on Next Generation Computing Applications, 2022. [3] P.Ribeiro, P. Orzechowski, J. B. Wagenaar, & J. H. Moore,\"BenchmarkingAutoMLalgorithmsonacollectionofsyntheticclassificationproblems,\"eprint arXiv:2212.02704, 2022. [4] M.Abdelaal, C. Hammacher, & H. Schoening, \"REIN:A Comprehensive Benchmark Framework for DataCleaning Methods in ML Pipelines,\" eprint arXiv:2302.04702, 2023. [5] F. Neutatz, B. Chen, Y. Alkhatib, J. Ye, & Z. Abedjan, \"Data Cleaning and AutoML: Would an Optimizer Choose to Clean?\" Eprint Springer s13222-022-00413-2, 2022. [6] M. Abdelaal, R. Koparde, & H. Schoening, \"AutoCure: Automated Tabular Data Curation Technique for ML Pipelines,\" eprint arXiv:2304.13636, 2023. [7] S. Holzer&K. Stockinger, \"Detectingerrorsin databases with bidirectional recurrent neural networks,\" OpenProceedings ZHAW, 2022. [8] P. Li, Z. Chen, X. Chu, & K. Rong, \"DiffPrep: Differentiable Data Preprocessing Pipeline Search for Learning over Tabular Data,\" eprint arXiv:2308.10915, 2023. [9] M. Singh, J. Cambronero, S. Gulwani, V. Le, C. Negreanu, & G. Verbruggen, \"DataVinci: Learning Syntactic and Semantic String Repairs,\" eprint arXiv:2308.10922, 2023. [10] S. Guha, F. A. Khan, J. Stoyanovich, & S. Schelter, \"Automated DataCleaningCanHurt FairnessinMachine Learning-based Decision Making,\" in IEEE 39th International Conference on Data Engineering, 2023. [11] R. Wang, Y. Li, & J. Wang, \"Sudowoodo: Contrastive Self-supervised Learning for Multi-purpose Data Integration and Preparation,\" eprint arXiv:2207.04122, 2022. [12] B. Hilprecht, C. Hammacher, E. Reis, M.Abdelaal, & C. Binnig, \"DiffML: End-to-end Differentiable ML Pipelines,\" eprint arXiv:2207.01269, 2022. [13] V. Restat, M. Klettke, & U. Störl, \"Towards a Holistic Data Preparation Tool,\" in EDBT/ICDT Workshops,2022. [14] M. Nashaat, A. Ghosh, J. Miller, & S. Quader, \"TabReformer:UnsupervisedRepresentationLearningfor ErroneousDataDetection,\"eprinthttps://doi.org/10.1145/3447541,2021. [15] F. Calefato, L. Quaranta, F. Lanubile, & M. Kalinowski, \"Assessing the Use ofAutoMLfor Data-Driven Software Engineering,\" eprint arXiv:2307.10774, 2023. [16] H. Stühler, M. A. Zöller, D. Klau, A. Beiderwellen- Bedrikow, & C. Tutschku, \"Benchmarking Automated Machine Learning Methods for Price Forecasting Applications,\" eprint arXiv:2304.14735, 2023 [17] M. Feurer, A. Klein, J. Eggensperger, Katharina Springenberg, M. Blum, F. Hutter, Efficient and robust automated machine learning, in: Advances in Neural InformationProcessingSystems28(2015),2015,pp.2962–2970. [18] E.LeDell,S.Poirier,H2OAutoML:Scalable automatic machine learning, 7th ICML Workshop on Automated Machine Learning (AutoML) (July2020). [19] P. Gijsbers, E. LeDell, S. Poirier, J. Thomas, B. Bischl, J. Vanschoren, An open source automl benchmark, 2019,6th ICML Workshop on Automated Machine Learning, AutoML@ICML2019 ; Conference date: 14-06-2019 Through 14-06-2019. [20] P. Gijsbers, M. L. P. Bueno, S. Coors, E. LeDell, S. Poirier, J. Thomas, B. Bischl, J. Vanschoren, Amlb: an automlbenchmark(2022).doi:10. 48550/ARXIV.2207.12560. [21] I. Guyon, L. Sun-Hosoya, M. Boull´e, H. J. Escalante, S. Escalera, Z. Liu, D. Jajetic, B. Ray, M. Saeed, M. Sebag,A. Statnikov, W.-W. Tu, E. Viegas, Analysis of the AutoML Challenge Series 2015–2018, Springer International Publishing, Cham, 2019, pp. 177– 219.doi:10.1007/978-3-030-05318-5_10. [22] Erickson N, Mueller J, ShirkovA, Zhang H, Larroy P, Li M, Smola AJ (2020) Autogluon-tabular: Robust and accurate automl for structured data. CoRR, abs/2003.06505 [23] K. Van der Blom, A. Serban, H. Hoos, and J. Visser, “AutoMLAdoptioninMLSoftware,8th ICMLWorkshop on Automated Machine Learning, 2021 [24] T. T. Le, W. Fu, J. H. Moore, Scaling tree-based automated machinelearningtobiomedicalbigdatawitha featuresetselector,Bioinformatics36(1)(2020)250–256. [25] M. Nashaat, A. Ghosh, J. Miller, and S. Quader,“TabReformer:Unsupervised Representation Learning for Erroneous Data Detection,”in ACM Transactions on benchmark, 2019, 6th ICML Workshop onAutomatedMachineLearning,AutoML@ICML2019; Conference date: 14-06-2019 Through 14-06-2019. .

Copyright

Copyright © 2025 Dr. V. Kavitha, Dr. R. G. Suresh Kumar, Ms. Divyapriya P, Ms. Haripriya C, Ms. Lavanya J. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.