Machine learning implementation in Python: Performance analysis of different libraries
Praggnya Kanungo · World Journal of Advanced Research and Reviews · 2023
This research paper presents a comprehensive performance analysis of popular machine learning libraries in Python. We compare the efficiency, accuracy, and scalability of scikit-learn, TensorFlow, PyTorch, and XGBoost across various machine learning tasks, including classification, regression, and clustering. The study evaluates these libraries using standardized datasets and benchmarks, considering factors such as execution time, memory usage, and model performance. Our findings provide valuable insights for data scientists and developers in selecting the most appropriate library for their specific machine learning projects. The results demonstrate that while scikit-learn excels in simplicity and ease of use for traditional machine learning tasks, TensorFlow and PyTorch offer superior performance for deep learning applications. XGBoost shows remarkable efficiency in gradient boosting tasks. This analysis aims to guide practitioners in making informed decisions when choosing machine learning libraries for their Python-based projects.