The Unsupervised Learning Workshop

4. Dimensionality Reduction Techniques and PCA

Overview

In this chapter, we will apply dimension reduction techniques and describe the concepts behind principal components and dimensionality reduction. We will apply Principal Component Analysis (PCA) when solving problems using scikit-learn. We will also compare manual PCA versus scikit-learn. By the end of this chapter, you will be able to reduce the size of a dataset by extracting only the most important components of variance within the data.

Introduction

In the previous chapter, we discussed clustering algorithms and how they can be helpful to find underlying meaning in large volumes of data. This chapter investigates the use of different feature sets (or spaces) in our unsupervised ...