This repository has a (hopefully growing) number of examples that shows how to implement the machine learning algorithm Random Forest in Processing using the OpenCV library for Processing.
Each of the examples have a README that describes the functionality of the example. All of the example source codes are also heavily annotated.
Random Forest is a machine learning algorithm that you can train to predict things.
You train the algorithm by giving it a bunch of tabular data, with answers. A very simple example would be a data set describing my (fictional) taste in movies.
action, romance, thriller, result
1, 0, 1, 1
0, 1, 0, 0
1, 0, 1, 1
As you can see, my data is all numbers. If we know that 1 means yes, and 0 means no, we can extrapolate that I really like movies in the thriller genre, whereas I'm not a big fan of romantic movies - even if they have thriller elements.
If you train the algorithm with this data, you can make it predict whether I would like a certain movie or not. We just need to know whether it has elements of action, romance or thriller, and the algorithm can help us.
When you've trained your algorithm, you can make it predict a result. In our movie example, we can now give it a row of data like this and have it predict whether I like the movie or not.
action, romance, thriller
0, 0, 1,
Huge thanks to @atduskgreg for creating the OpenCV lirabry for Processing.