Machine Learning with R, the tidyverse, and mlr

Chapter 6. Classifying with naive Bayes and support vector machines

This chapter covers

• Working with the naive Bayes algorithm
• Understanding the support vector machine algorithm
• Tuning many hyperparameters simultaneously with a random search

The naive Bayes and support vector machine (SVM) algorithms are supervised learning algorithms for classification. Each algorithm learns in a different way. The naive Bayes algorithm uses Bayes’ rule, which you learned about in chapter 5, to estimate the probability of new data belonging to one of the classes in the dataset. The case is then assigned to the class with the highest probability. The SVM algorithm looks for a hyperplane (a surface that has one less dimension than there are predictor variables) ...