Mlrl-boomer

A bugfix release that solves the following issues:

- Fixes an issue preventing the use of dense representations of ground truth label matrices that was introduced in version 0.7.0.
- Pre-built packages for macOS systems are now available at [PyPI](https://pypi.org/project/mlrl-boomer/).
- Linux and macOS packages for Python 3.10 are now provided.

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

- L1 regularization can now be used.
- A more space-efficient data structure is now used for the sparse representation of binary predictions.
- The Python API does now allow to access the rules in a model in a programmatic way.
- It is now possible to output certain characteristics of training datasets and rule models.
- Pre-built packages for the Linux platform are now available at [PyPI](https://pypi.org/project/mlrl-boomer/).
- The [documentation](https://mlrl-boomer.readthedocs.io) has vastly been improved.

A bugfix release that solves the following issues:

- Fixes a segmentation fault when a sparse feature matrix should be used for prediction that was introduced in version 0.6.0.

A bugfix release that solves the following issues:

- Fixes a mathematical problem when calculating the quality of potential single-label rules that was introduced in version 0.6.0.

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

{warning}
This release comes with changes to the command line API. For brevity and consistency, some parameters and/or their values have been renamed. Moreover, some parameters have been updated to use more reasonable default values. For an updated overview of the available parameters, please refer to the [documentation](https://mlrl-boomer.readthedocs.io/en/0.6.0/).


- The parameter `--instance-sampling` does now allow to use stratified sampling (`stratified-label-wise` and `stratified-example-wise`).
- The parameter `--holdout` does now allow to use stratified sampling (`stratified-label-wise` and `stratified-example-wise`).
- The parameter `--recalculate-predictions` does now allow to specify whether the predictions of rules should be recalculated on the entire training data, if instance sampling is used.
- An additional parameter (`--prediction-format`) that allows to specify whether predictions should be stored using dense or sparse matrices has been added.
- The code for the construction of rule heads has been reworked, resulting in minor performance improvements.
- The unnecessary calculation of Hessians is now avoided when used single-label rules for the minimization of a non-decomposable loss function, resulting in a significant performance improvement.
- A programmatic C++ API for configuring algorithms, including the validation of parameters, is now provided.
- A documentation is now available [online](https://mlrl-boomer.readthedocs.io).

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

- Gradient-based label binning (GBLB) can be used to assign labels to a predefined number of bins.