In this chapter, we've learned about how to make use of categorical variables to group data into classes.
We've seen how quantify the difference between groups using the odds ratio and relative risk, and how to perform statistical significance tests on groups using the X2 test. We've learned about how to build machine learning models suitable for the task of classification with a variety of techniques: logistic regression, naive Bayes, decision trees, and random forests, and several methods of evaluating them; the confusion matrix and the kappa statistic. We also learned about the opposing dangers of high bias and of overfitting in machine learning, and how to ensure that your model is not overfitting by making use of cross-validation. Finally, we've seen how the clj-ml library can help to prepare data and to build many different types of classifiers and save them for future use.
In the next chapter, we'll learn about how to adapt some of the techniques we've learned about so far to...