Installation¶
All algorithms that are provided as part of this project are published on PyPi. As shown below, they can easily be installed via the Python package manager pip. Unless you intend to modify the algorithms’ source code, in which case you should have a look at the section Building from Source, this is the recommended way for installing the software.
Note
Currently, the packages mentioned below are available for Linux (x86_64 and aarch64), MacOS (x86_64) and Windows (AMD64).
Examples of how to use the algorithms in your own Python programs can be found in the section Using the Python API.
Installing the BOOMER Algorithm¶
The gradient boosting algorithm BOOMER is published as the Python package mlrl-boomer. It can be installed via the following command:
pip install mlrl-boomer
The description of the methodology used by the BOOMER algorithm, as well as examples of how to configure it, are given here.
Installing the SeCo Algorithm¶
In addition to the BOOMER algorithm, this project does also provide a Separate-and-Conquer (SeCo) algorithm based on traditional rule learning techniques that are particularly well-suited for learning interpretable models. It is published as the package mlrl-seco and can be installed as follows:
pip install mlrl-seco
In this section, we elaborate on the techiques utilized by the SeCo algorithm and discuss its parameters.
Installing the Command Line API¶
To ease the use of the algorithms that are developed by this project, we also provide a command line API that allows to configure the algorithms and apply them to a given dataset without the need to write code. It is published as the Python package mlrl-testbed and can optionally be installed via the following command:
pip install mlrl-testbed
For more information about how to use the command line API, refer to the section Using the Command Line API.