There are a number of things that we can explore in Go. Here's a non-exhaustive list of some things you may want to explore:
- Random trees and random forests
- Support vector machines
- Gradient-boosting methods
- Maximum-entropy methods
- Graphical methods
- Local outlier factors
Perhaps if there is a second edition to this book, I will cover them. If you are familiar with machine learning methods, you may note that these, especially the first three, are perhaps some of the highest-performing machine learning methods, when compared with the things written in this book. You might wonder why they were not included. The schools of thought that these methods belong to might supply a clue.
For example, random trees and random forests can be considered pseudo-Symbolist—they're a distant cousin of the Symbolist school of thought, originating from...
