Chapter 11. Predictions Don't Grow on Trees - or Do They?
Our goal in this chapter is to see and apply the concepts learned about in the previous chapters in order to construct and use modern learning algorithms in order to glean insights and make predictions on real datasets. While we explore the following algorithms, we should always remember that we are constantly keeping our metrics in mind.
In this chapter, we will be looking at the following machine learning algorithms:
- Decision trees
- Naive Bayes classification
- k-means clustering
The first two are examples of supervised learning, while the final algorithm is an example of unsupervised learning.
Let's get to it!
