Which package is commonly used in Python for machine learning?

Scikit-learn is widely recognized as one of the most popular Python packages for machine learning. It provides a robust set of tools and algorithms for various machine learning tasks, including classification, regression, clustering, and model selection. This library is built on NumPy, SciPy, and Matplotlib, which means it can efficiently handle numerical data and also allows for effective data visualization.

Scikit-learn is designed to be user-friendly and accessible, making it an excellent choice for both beginners and experienced practitioners. It includes well-documented components that facilitate tasks such as preprocessing data, training models, and evaluating results. The package also supports various machine learning workflows and is often used in conjunction with other libraries to create comprehensive data science and machine learning solutions.

In contrast, while Pandas is a powerful library for data manipulation and analysis, it does not provide machine learning algorithms itself. NumPy is essential for numerical computations but is primarily focused on array and matrix operations. Matplotlib serves as a plotting library that helps visualize data but does not handle machine learning tasks directly. Thus, Scikit-learn's specific focus on machine learning makes it the appropriate choice in this context.