Section contents In this section, we introduce the

The problem solved in supervised learning

Pipelining We have seen that some estimators can transform data and that some estimators can predict variables. We can also create combined estimators:

Often the hardest part of solving a machine learning problem can be finding the right estimator for the job. Different estimators are better suited for different types of data and different

Statistical learning

Datasets Scikit-learn deals with learning information from one or more datasets that are represented as 2D arrays. They can be understood as a list of multi-dimensional observations

Clustering: grouping observations together The problem solved in clustering Given the

Score, and cross-validated scores As we have seen, every estimator exposes a score method that can judge the

The goal of this guide is to explore some of the main scikit-learn tools on a single practical task: analysing a collection of text documents (newsgroups

An introduction to machine learning