Mastering Machine Learning with R - Second Edition

Summary

In this chapter, we took a second stab at unsupervised learning techniques by exploring PCA, examining what it is, and applying it in a practical fashion. We explored how it can be used to reduce the dimensionality and improve the understanding of the dataset when confronted with numerous highly correlated variables. Then, we applied it to real data from the National Hockey League, using the resulting principal components in a regression analysis to predict total team points. Additionally, we explored ways to visualize the data and the principal components.

As an unsupervised learning technique, it requires some judgment along with trial and error to arrive at an optimal solution that is acceptable to business partners. Nevertheless, ...