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@michael-rapp michael-rapp released this 09 Aug 23:39
· 233 commits to feature since this release
0.11.0
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Learn about vigilant mode.

A major update to the BOOMER algorithm that introduces the following changes.

This release comes with several API changes. For an updated overview of the available parameters and command line arguments, please refer to the documentation.

Algorithmic Enhancements

  • The BOOMER algorithm can be used for solving regression problems, including single- and multi-output regression problems.

Additions to the Command Line API

  • Custom algorithms can now be easily integrated with the command line API due to the ability to dynamically load code from your own Python modules or source files, as illustrated here
  • The value to be used for sparse elements in the feature matrix can be specified via the argument --sparse-feature-value.

API Changes

  • The Python module or source file providing an integration with the machine learning algorithm to be used by the command line API must now be specified as described here.
  • Several parameters and their values have been renamed to better reflect the scope of the project, which now includes multi-output regression problems. For an up-to-date list of parameters, please refer to the documentation.
  • Rules with complete heads are now learned by default when using a decomposable loss function and a dense format for storing statistics.
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