An Empirical Evaluation of ChatGPT as an Automated Machine Learning Code Generator for Image Classification - Dedi Mardianto; Milla Apriliana; Eva Oktavia; Ideva Gaputra; Widya Wahyuni (Ikatan Ahli Informatika Indonesia Nusantara, 2025)
The emergence of large language models such as ChatGPT has created unprecedented opportunities for automating software development processes, particularly within the machine learning domain. This study aims to empirically evaluate the effectiveness of ChatGPT in generating machine learning code for image classification tasks using the Keras framework. The research employs an experimental methodology utilizing the MNIST dataset, comprising 70,000 handwritten digit images. A systematic series of experiments was conducted through progressive prompting strategies, ranging from basic model construction to comprehensive evaluation protocols. The findings demonstrate that ChatGPT successfully generated 100% executable code without errors, with the resulting models achieving 99% accuracy on the test dataset. A notable discovery emerged in the form of