Hands-On Data Science and Python Machine Learning

Dimensionality reduction

So, what is the curse of dimensionality? Well, a lot of problems can be thought of having many different dimensions. So, for example, when we were doing movie recommendations, we had attributes of various movies, and every individual movie could be thought of as its own dimension in that data space.

If you have a lot of movies, that's a lot of dimensions and you can't really wrap your head around more than 3, because that's what we grew up to evolve within. You might have some sort of data that has many different features that you care about. You know, in a moment we'll look at an example of flowers that we want to classify, and that classification is based on 4 different measurements of the flowers. Those 4 different ...