Dimensionality Reduction, Feature Selection, and t-SNE

Before we dive deeper into the theory of machine learning, it is good to realize that we have only scratched the surface of machine learning tools in the sklearn toolbox. Besides classification, there is of course regression, where the label is a continuous variable instead of a categorical one. We will later see that we can formulate most supervised machine learning techniques as regression and that classification is only a special case of regression.

Sklearn also includes several techniques for clustering, which are often unsupervised learning techniques to discover relations in data. Popular examples are k-means and Gaussian mixture models $\text {(GMM)}$. We will discuss such techniques and unsupervised learning more in later chapters. Here, we will end this lesson by discussing some dimensionality reduction methods.