Overview of scikit-learn

Suppose you want to apply machine learning to a problem in Python. You need algorithms that learn patterns from data, and you need to understand how each of those algorithms is meant to be used. Writing every algorithm yourself is slow and error-prone, so practitioners usually rely on a library: a collection of tested, reusable tools that someone else has already built.

scikit-learn is one such library. This article answers three questions before you go further into the chapter: what scikit-learn is, what it provides, and why it is worth knowing about. Keep the first question in mind as you read, because the answer shapes how you should approach everything else.

The phrase open source tells you something about access, not only about cost. Because the source code is available to anyone, you are not limited to trusting a black box. If you want to know what a particular step is doing behind the scenes, the code that performs that step can, in principle, be read. This transparency is one of the features that makes the library valuable to people who want to understand machine learning rather than just call functions that produce results.

Open source also means the project is not frozen at one moment in time. The source describes scikit-learn as constantly being developed and improved, with a very active user community. Improvements arrive continually, and the people who use the library also take part in shaping it.

scikit-learn gives you two main things: machine learning algorithms and documentation. They are linked. The algorithms give you working tools, and the documentation explains what each tool is for and what options it offers. A diagram of this relationship follows.

The source gives a concrete instruction for reading this material. While you read the chapter, browse the scikit-learn user guide and API documentation for more detail and for the many options available for each algorithm. The online documentation is very thorough, and the chapter supplies the machine learning prerequisites needed to understand it in depth. The flowchart below shows how these resources fit into your working sequence.

The source describes scikit-learn as a very popular tool and as the most prominent Python library for machine learning. It is widely used in both industry and academia. These two settings have different goals. Companies usually want models that solve business problems, while universities and research groups often want to study and compare methods.

A library that serves both audiences has to be accurate, well documented, and easy to learn from. Its documentation for each algorithm, its open source code that can be examined, and its continuing development together account for much of its appeal.

scikit-learn also works well with a number of other scientific Python tools. Keep this in mind as you learn: scikit-learn is usually one piece of a larger working setup rather than a complete environment by itself. The chapter discusses those other tools later.