https://github.com/runemadsen/random-forest-processing-opencv
Java-implementation of a Random Forest algorithm in Processing using OpenCV
https://github.com/runemadsen/random-forest-processing-opencv
Last synced: 2 months ago
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Java-implementation of a Random Forest algorithm in Processing using OpenCV
- Host: GitHub
- URL: https://github.com/runemadsen/random-forest-processing-opencv
- Owner: runemadsen
- Created: 2013-09-11T02:14:13.000Z (over 11 years ago)
- Default Branch: master
- Last Pushed: 2013-09-22T22:33:04.000Z (over 11 years ago)
- Last Synced: 2025-04-01T21:51:58.152Z (2 months ago)
- Language: Processing
- Homepage: www.runemadsen.com
- Size: 453 KB
- Stars: 10
- Watchers: 2
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Random Forest in Processing and OpenCV
======================================
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.
Examples
--------
Each of the examples have a README that describes the functionality of the example. All of the example source codes are also heavily annotated.
What is Random Forest?
----------------------
Random Forest is a machine learning algorithm that you can train to predict things.
Training
--------
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.
```csv
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.
Predicting
----------
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.
```csv
action, romance, thriller
0, 0, 1,
```
Huge thanks to @atduskgreg for creating the OpenCV lirabry for Processing.