Kampezidou, S.I.; Tikayat Ray, A.; Bhat, A.P.; Pinon Fischer, O.J.; Mavris, D.N. Fundamental Components and Principles of Supervised Machine Learning Workflows with Numerical and Categorical Data. Eng2024, 5, 384-416.
Kampezidou, S.I.; Tikayat Ray, A.; Bhat, A.P.; Pinon Fischer, O.J.; Mavris, D.N. Fundamental Components and Principles of Supervised Machine Learning Workflows with Numerical and Categorical Data. Eng 2024, 5, 384-416.
Kampezidou, S.I.; Tikayat Ray, A.; Bhat, A.P.; Pinon Fischer, O.J.; Mavris, D.N. Fundamental Components and Principles of Supervised Machine Learning Workflows with Numerical and Categorical Data. Eng2024, 5, 384-416.
Kampezidou, S.I.; Tikayat Ray, A.; Bhat, A.P.; Pinon Fischer, O.J.; Mavris, D.N. Fundamental Components and Principles of Supervised Machine Learning Workflows with Numerical and Categorical Data. Eng 2024, 5, 384-416.
Abstract
This paper offers a comprehensive examination of the process involved in developing and automating supervised end-to-end machine learning workflows for forecasting and classification purposes. It offers a complete overview of the components (i.e. feature engineering, model selection, etc), principles (i.e. bias-variance decomposition, model complexity, overfitting, model sensitivity to feature assumptions and scaling, output interpretability, etc), models (i.e. neural networks, regression models, etc), methods (i.e. Cross-Validation, data augmentation, etc), metrics (i.e. Mean Squared Error, F1-score, etc) and tools that rule most supervised learning applications with numerical and categorical data, as well as their integration, automation, and deployment. The end goal and contribution of this paper is the education and guidance of the non-AI expert academic community over complete and rigorous machine learning pipelines and data science practices, from problem scoping to design and state-of-the-art automation tools, including basic principles and reasoning in the choice of methods. The paper delves into the critical stages of supervised machine learning workflow development, many of which are often omitted by researchers due to brevity, and covers foundational concepts essential for understanding and optimizing a functional machine learning workflow, thereby offering a holistic view of task-specific application development for applied researchers who are not AI experts.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
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.
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