COMPARATIVE ANALYSIS OF AUTOMATED MACHINE LEARNING LIBRARIES: PYCARET, H2O, TPOT, AUTO-SKLEARN, AND FLAML

Automated Machine Learning (AutoML) frameworks have gained significant popularity in recent years, making machine learning accessible to a broader audience by automating many of the tasks traditionally performed by data scientists and domain experts. This paper presents a comparative analysis of five prominent AutoML libraries: PyCaret, H2O.ai, TPOT, Auto-sklearn, and FLAML. Each of these libraries provides automated solutions for model selection, hyperparameter tuning, and other machine learning tasks. The goal of this study is to assess their performance, ease of use, flexibility, and suitability for various types of machine learning problems. The comparison is based on multiple factors, including ease of use, performance, customization, resource efficiency, and suitability. This research aims to help researchers, practitioners, and developers in selecting the most appropriate AutoML library based on their specific needs and resources. Keywords: AutoML, PyCaret, H2O.ai, TPOT, Auto-sklearn, FLAML