Working with scikit-learn's machine learning algorithms

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. For that reason, practitioners usually rely on a library: a collection of tested, reusable tools that someone else has already built.

scikit-learn is one such library. It is an open source Python library for machine learning, and it is free to use and distribute. 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.

scikit-learn offers two main things, and they belong together. The first is a set of state-of-the-art machine learning algorithms. The second is comprehensive documentation for each of those algorithms. The algorithms give you working tools, and the documentation explains what each tool is for and what options it offers.

Notice what the source does not do. It describes the algorithms only as state-of-the-art and does not list which specific algorithms the library contains. For that level of detail, the documentation is the right place to look. This division of labor is why the source recommends the documentation so strongly.

Because the source code is available, 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 only call functions that return results.

Open source projects of this kind are also 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.

The source gives a concrete instruction for reading this material. While you read the chapter, you should also 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 two resources are meant to be used together, not one after the other.

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, and it works well with a number of other scientific Python tools, which the chapter discusses later.

Those two settings have different goals. Companies usually want models that solve business problems. 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. The combination of state-of-the-art algorithms, thorough documentation, and open source access is what allows one library to serve both.