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This comprehensive benchmarking study explores the performance of three prominent machine learning libraries: PyTorch, Keras with TensorFlow backend, and Scikit-learn with the same criteria, software, and hardware. The evaluation encompasses two diverse datasets, "student performance" and "College Attending Plan Classification," supported by Kaggle platforms utilizing feedforward neural networks (FNNs) as the modeling technique. The findings reveal that PyTorch and Keras with TensorFlow backend excel on the "College Attending Plan Classification" dataset, with PyTorch achieving impeccable precision, Recall, and F1-score for both classes. While Scikit-learn demonstrates commendable performance, it trails behind these libraries in this context. On the "Student Performance" dataset, all three libraries deliver comparable results, with Scikit-learn exhibiting the lowest accuracy at 16%. Keras with TensorFlow backend and PyTorch attain accuracy rates of 23%, respectively. Moreover, this study offers valuable insights into each library's unique strengths and weaknesses when confronted with diverse dataset types. PyTorch emerges as the go-to choice for demanding tasks requiring high performance, while Scikit-learn proves advantageous for simpler tasks with modest computational demands. Keras with TensorFlow backend strikes a balance between performance and user-friendliness. This benchmarking endeavor equips machine learning practitioners with valuable guidance for selecting the most suitable library or framework tailored to their project requirements. It underscores the pivotal role of library choice in achieving optimal results in machine learning endeavors.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
* https://www.kaggle.com/datasets/charleyhuang1022/college-plan
* https://www.kaggle.com/datasets/joebeachcapital/students-performance/