In this chapter, we will reduce the number of features or inputs into the machine learning models. This is a very important operation because sometimes datasets have a lot of input columns, and reducing the number of columns creates simpler models that take less computing power to predict.
The main model used in this section is principal component analysis (PCA). You do not have to know how many features you can reduce the dataset to, thanks to PCA's explained variance. A similar model in performance is truncated singular value decomposition (truncated SVD). It is always best to first choose a linear model that allows you to know how many columns you can reduce the set to, such as PCA or truncated SVD.
Later in the chapter, check out the modern method of t-distributed stochastic neighbor embedding (t-SNE), which makes features easier to visualize in lower dimensions...
