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 mentioned in the Preface. 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 combines linearly 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 a guess for the number of clusters, while other algorithms don't.

The first step in genetic algorithms is to initialize the population with random individuals and related representation of genetic information. During each generation, individuals are selected for mating based on a predefined fitness function. The application of decision trees in machine learning is called decision tree learning.

The next chapter, Chapter 11, Environments Outside the Python Ecosystem and Cloud Computing, describes interoperability and Cloud possibilities.