Machine Learning Quick Reference

Introduction

In the previous chapter, we understood what principal component analysis (PCA) is, how it works, and when we should be deploying it. However, as a dimensionality reduction technique, do you think that you can put this to use in every scenario? Can you recall the roadblock or the underlying assumption behind it that we discussed?

Yes, the most important assumption behind PCA is that it works for datasets that are linearly separable. However, in the real world, you don't get this kind of dataset very often. We need a method to capture non-linear data patterns.

On the left-hand side, we have got a dataset in which there are two classes. We can see that once we arrive at the projections and establish the components, PCA doesn't ...