Summary

This chapter explored principal component analysis, including how it works and when we might want to use it. We learned how to examine the components created from PCA, including how each feature contributes to each component, and how much of the variance is explained. We went over how to visualize components and how to use components in subsequent analysis. We also examined how to use kernels for PCA and when that might give us better results.

We explore another unsupervised learning technique in the next chapter, k-means clustering.