Learning speed or prediction accuracy? Comparative analysis of program-ming frameworks for artificial intelligence
Abstract
The purpose of the article is to analyze frameworks for artificial intelligence applications. In particular, the effectiveness, time-consumption and resources requirement. Linear regression, random forests and k nearest neighbors models were created for each framework. The learning data is a dataset containing informations about diamonds and their prices. Each model was designed to learn diamonds’ prices and then make a prediction depending on its specific characteristics such as cut, color, and volume. The learning data was divided into sets of different sizes to show changes in a model depending on the amount of training data.
Keywords:
artificial intelligence; data prediction; frameworkReferences
X. D. Zhang, A Matrix Algebra Approach to Artificial Intelligence, Springer, 2020.
DOI: https://doi.org/10.1007/978-981-15-2770-8
Google Scholar
J. Brownlee, Deep learning for computer vision: image classification, object detection, and face recognition in python, Machine Learning Mastery, 2019.
Google Scholar
R. Garreta, G. Moncecchi, Learning scikit-learn: machine learning in python, PACKT Publishing Ltd., 2013.
Google Scholar
G. Hackeling, Mastering Machine Learning with scikit-learn, PACKT Publishing Ltd., 2017.
Google Scholar
E. Stevens, L. Antiga, T. Viehmann, Deep learning with PyTorch, Manning Publications, 2020.
Google Scholar
N. McClure, TensorFlow machine learning cookbook, PACKT Publishing Ltd., 2017.
Google Scholar
D. Sarkar, R. Bali, T. Sharma, Practical machine learning with Python. A Problem-Solvers Guide To Building Real-World Intelligent Systems, Apress, Berkeley, CA, 2018.
DOI: https://doi.org/10.1007/978-1-4842-3207-1
Google Scholar
S. Raschka, V. Mirjalili, Python Machine Learning: Machine Learning and Deep Learning with Python. Scikit-Learn, and TensorFlow, Second edition, PACKT Publishing Ltd., 2017.
Google Scholar
Authors
Piotr ŻukiewiczPoland
Authors
Edyta ŁukasikPoland
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