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
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Authors
Piotr ŻukiewiczPoland
Authors
Edyta ŁukasikPoland
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