Three machine learning toolkits are shotly described and available
below: CORElearn, SemiArtificial
The system supports feature evaluation, decision, regression
trees, random forests, OrdEval algorithm, explanation of
predictions and some machine learning utillities. The source
code and binaries of the learning system CORElearn can be
- port to R
system available via CRAN: sources and also binaries for
different platforms (regularly tested tested for Windows,
Linux, and OS X)
- the latest development version updated 09
July, 2016: CORElearn_1.48.0.tar.gz
version (version 0.9.44, 25 January, 2015) consists of
the same C++ code, plus compilation instructions, parameters
file, data format description, and same sample testing data.
It is tested to compile with gcc and msvc and contains Windows
executable. It may be older than the R port, mail me if you
need a fresh one. To compile the sources read the ReadMe.htm
file first. Supported data input formats are C4.5, C5, and
native format. The system is tested to compile and run on
Windows and Linux. The documentation is available
through R, just install the package and type ?CORElearn at R
CORElearn is available under the GNU public license (GPLv3 or later).
Other licensing might be possible.
Some of the functionality and features are described in my papers. The R package contains the
documentation which applies also to the standalone version.
The system for generating new data based on
properties of existing one is available
CRAN. Current development version is semiArtificial 2.0.1
(September 4th, 2015). The working principles are described in my
The package allows explanation of predictions
of a given prediction model. Two explanation methods (EXPLAIN
and IME) and two levels of prediction explanations are
- an instance level explanation, which shows contributions of
features to the decision of the model,
- a model level explanation, which average explanations of
individual instances to create a weighted summary explanation
of the whole model.
The package is available
. Current development version is ExplainPrediction 1.1.7
(16 March, 2017) and windows
. The working principles are described in my
Home page of Marko Robnik Šikonja.