The scikit-learn modules for different models

The scikit-learn library is organized into submodules. Each submodule contains algorithms and helper methods for a certain class of machine learning models and approaches.

Here is a sample of those submodules, including some example models:

While these approaches are perse, a scikit-learn library abstracts away a lot of differences by exposing a regular interface to most of these algorithms. All of the example algorithms listed in the table implement a fit method, and most of them implement predict as well. These methods represent two phases in machine learning. First, the model is trained on the existing data with the fit method. Once trained, it is possible to use the model to predict the class or value of unseen data with predict. We will see both the methods at work in the next sections.

The scikit-learn library is part of the PyData ecosystem. Its codebase has seen steady growth over the past six years, and with over hundred contributors, it is one of the most active and popular among the scikit toolkits.