The SciPy package essentially kicked off the entire era of scientific Python. Created in 2001 by researchers Travis Oliphant, Pearu Peterson, and Eric Jones, it was formed as a collection of basic and universal scientific techniques. Over time, the package grew and now offers generic tooling and popular techniques for scientific analysis. Its submodules cover linear algebra, integration, optimization, interpolation, statistics, and many more.
With the rise of machine learning, the corresponding submodule of SciPy grew more and more complex. At some point, it became so big, the decision was made to reintroduce it as a separate, independent package—scikit-learn. As the mark of its origins, the package kept its name, defined earlier as SciPy kit—learn. Due to its simple and unified interface and a large variety of models, scikit-learn quickly...