This library is reasonably efficient, meaning that the complexity of the implementation of the algorithms involved is that given by the theory. However, when a choice must be made between code readability and efficiency, readability has been chosen. It is thus ideal in research (because algorithms can easily be modified) and as an academic tool (students can quickly get interesting results). Its general philosophy can be compared to that of the jahmm library (an implementation of Hidden Markov Models in Java).
This page is not an introduction to decision tree. The reader should have a (basic) idea of what a decision tree is.
|0.5.0 (sources tar.gz or zip, jar)||Online, tar.gz, zip||October 2004||First public release.|
|0.5.1 (sources tar.gz or zip, jar)||Online, tar.gz, zip||November 2004||Various improvements.|
|0.6.0 (sources tar.gz or zip, jar)||Online, tar.gz, zip||November 2004||Handles unknown values.|
|0.6.1 (sources tar.gz or zip, jar)||Online, tar.gz, zip||November 2004||Bugfixes.|
The changelog summarizes the changes since the first version. Please contact Jean-Marc François (the author) at the address francois-jaDTi@run.montefiore.ulg.ac.be if you have any comment or question.