Practical Data Analysis - Second Edition

Summary

In this chapter, we got into dimensionality reduction and linear classification using SVM. In our example, we created a simple but powerful SVM classifier using different kinds of kernels, and you learned how to perform a dimensionality reduction using PCA implemented in Python with mlpy. Finally, we presented how to use nonlinear kernels, such as Gaussian or Polynomial. The work in this chapter was just an introduction to the SVM algorithm, with only two dimensions. The results can be improved with a multidimensional approach with an optimal hyperplane.

In the next chapter, you will learn how to model an epidemiological event (infectious disease) and how an infectious disease is spread through a population. We will create a simulator of ...