Feature: Quantile Scaler

Description

This pr introduces a quantile based scaler. The QuantileScaler class introduced in this PR provides a robust method for scaling features to a target distribution using quantile information. This approach is particularly useful for datasets with outliers or non-normal distributions. It maps n quantile intervals of the source distribution onto n quantile intervals of a Gaussian distribution. This preserves the internal structure of the data while allowing for applying different transformations depending on the quantile interval.

Which issue does this PR tackle?

Common Min-Max/Mean scalers are not robust to outliers and multiple scales.

How does it solve the problem?

Implements QuantileScalerclass.

How are the changes tested?

Added unit-tests for QuantileScaler.

Checklist for Contributors

[x] Scope: This PR tackles exactly one problem.
[x] Conventions: The branch follows the feature/title-slug convention.
[x] Conventions: The PR title follows the Bugfix: Title convention.
[x] Coding style: The code passes all pre-commit hooks.
[x] Documentation: All changes are well-documented.
[x] Tests: New features are tested and all tests pass successfully.
[x] Changelog: Updated CHANGELOG.md for new features or breaking changes.
[x] Review: A suitable reviewer has been assigned.

Checklist for Reviewers:

[x] The PR solves the issue it claims to solve and only this one.
[x] Changes are tested sufficiently and all tests pass.
[x] Documentation is complete and well-written.
[x] Changelog has been updated, if necessary.

aai-institute / continuiti