Performing dimensionality data reduction
When we need to reduce the number of attributes (columns) as opposed to the number of data objects (rows), we have a case of dimensionality reduction. This is also known as dimension reduction. In this section, we will cover six methods: regression, decision tree, random forest, computational dimension reduction, functional data analysis (FDA), and principal component analysis (PCA).
Before we talk about each of them, we must note that there are two types of dimension reduction methods: supervised and unsupervised. Supervised dimension reduction methods aim to reduce the dimensions to help us predict or classify a dependent attribute. For instance, when we applied a decision tree algorithm to figure out which multi-variate patterns can predict customer churning, earlier in this chapter, we performed a supervised dimensionality reduction. The attributes that did not show up on the tree in Figure 13.2 are not important for predicting (classifying...
