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October 15, 2025Applied SciencesOpen Access

Large Language Models for Machine Learning Design Assistance: Prompt-Driven Algorithm Selection and Optimization in Diverse Supervised Learning Tasks

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Models utilizing effective prompting strategies can yield competitive results in various supervised learning tasks.
Hyperparameter tuning significantly affects the performance and optimization of machine learning models generated by LLMs.
The study evaluates LLM outputs against Kaggle user solutions and established AutoML frameworks, revealing vital insights.
Findings suggest that LLMs can be leveraged not just for code generation but also for designing effective machine learning solutions.

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Analysis reveals that effective hyperparameter tuning and prompting strategies improve algorithm selection in supervised learning tasks.

Fidan Kaya Gülağız (2025) studied this question.

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