In this chapter, we learned all about kNN classifiers and how to train them. We looked at decision trees and how to fit and visualize it. Then, we learned about random forests and how to train them. We looked at Naive Bayes classifiers, and trained one using the Titanic dataset. We then used SVMs on the Titanic dataset and learned how they work. We also looked at logistic regression. Finally, we learned how to find out multiple outcomes for all the classifiers that we worked on in this chapter.
In this next chapter, we will move on to regression, where we want to predict the value of a continuous variable, not a discrete class.
