In the previous chapter, we apply t-SNE to visualize the newsgroup text data in reduced 2 dimensions. T-SNE, or dimensionality reduction in general, is a type of unsupervised learning. Instead of having a teacher educating what particular output to produce, be it a class or membership (classification), be it a continuous value (regression), unsupervised learning identifies inherent structures or commonalities in the input data. Since there is no guidance in unsupervised learning, there is no clear answer on what is a right or wrong result. Unsupervised learning has the freedom to discover hidden information underneath input data.
An easy way to understand unsupervised learning is to think of going through many practice questions for an exam. In supervised learning, you are given answers to those practice questions. You basically...
