Chapter 5. Reducing Dimensions
In this chapter, we will cover various techniques to reduce dimensions of your data. You will learn the following recipes:
- Creating three-dimensional scatter plots to present principal components
- Reducing the dimensions using the kernel version of PCA
- Using Principal Component Analysis to find things that matter
- Finding the principal components in your data using randomized PCA
- Extracting the useful dimensions using Linear Discriminant Analysis
- Using various dimension reduction techniques to classify calls using the k-Nearest Neighbors classification model
Introduction
The abundance of data available nowadays can be mind-boggling; the datasets grow not only in terms of the number of observations, but also get richer in terms ...
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