Once we have found or collected enough (text, label) pairs, we can train a classifier. For the underlying structure of the classifier, we have a wide range of possibilities, each of them having advantages and drawbacks. Just to name some of the more prominent choices, there are logistic regression, decision trees, SVMs, and Naïve Bayes. In this chapter, we will contrast the instance-based method from the Chapter 2, Classifying with Real-world Examples, nearest-neighbor, with model-based logistic regression.