Summary
This chapter was devoted to predictive modeling and machine learning. These are very large fields to cover in one chapter, so you may want to have a look at some of the books from Packt Publishing (see http://www.packtpub.com). Predictive analytics uses a variety of techniques, including machine learning, to make useful predictions--for instance, to determine whether it is going to rain tomorrow.
SVM maps the data points to points in multidimensional space. The classification problem is then reduced to finding a hyperplane or hyperplanes that best separate the points into classes.
The elastic net regularization linearly combines the LASSO and ridge methods. For regression problems, goodness-of-fit is often determined with the coefficient of determination, also called R squared. Some clustering algorithms require an estimation of the number of clusters, while other algorithms don't.
The first step in genetic algorithms is to initialize the population with random individuals and...
