• Java Web Open Source Project
• Decision Tree Implementation In Java

Feb 03, 2015  Kind of old post but there is a very primitive Java based decision tree AI on Github.

Great implementation and explanation. I am now trying to extend this to handle a large number of continuous valued features in a random forest implementation. I took you getFeatures method and had it return a list of features that included EVERY single continuous valued attribute in my data set with the predicate being moreThanD(attrvalue). It seems like this is way too many and using a random forest implementation, I’d like to run each tree until the homogeneityPercentage is at 1.0, but it’s seemingly taking WAY too long. My random forest seems to get more and more accurate as I increase the depth though.

Java Web Open Source Project

Decision Tree Implementation In Java

Do you have any tips as to how I could adapt this to be more accurate and efficient. Thanks for any help. Hi Alex, after the post I’ve started the project where I’ve added simple random forest implementation and also added simple support for distributed computation with apache Ignite (I like it a lot, looks like really nice and simple framework). You could check it out at and maybe it will give you some ideas. It is significantly expanded with more features as I planned to make it a simple decision tree framework, but unfortunately started new job and didn’t have much time to finish. I wanted to implement most of features from this book:Be careful increasing depth by too much, as your model might start over-fitting (finding correct depth, or performing prunning(not implemented yet) will fight it).

If code is just too slow (both this and from Oak3ML with multiple threads/computers) then try to turn on some java profiler and we might think how to improve the bottleneck. If you have any questions about the code let me know and I will try to help.