v0.1.1

✨ New Features: Scikit-Learn Compatible Estimators

We are excited to introduce full scikit-learn compatibility! ReHLine now provides plq_Ridge_Classifier and plq_Ridge_Regressor estimators that integrate seamlessly with the entire scikit-learn ecosystem.

This means you can:

• Drop ReHLine estimators directly into your existing scikit-learn Pipeline.
• Perform robust hyperparameter tuning using GridSearchCV.
• Use standard scikit-learn evaluation metrics and cross-validation tools.

What's Changed

• update 'verbose' option by @Leona-LYT in #14
• add path solution and warm-start examples by @Leona-LYT in #15
• fix layout issue by @Leona-LYT in #16
• Update constraint.rst by @DataboyUsen in #17
• add CQR_Ridge_path_sol with examples by @Leona-LYT in #18
• modified _make_constraint_param by @Leona-LYT in #19
• modified sen_idx and add _sklearn_mixin.py by @Leona-LYT in #20
• delete _make_fair_classification function and modified related test file by @Leona-LYT in #22
• Update _base.py by @DataboyUsen in #21
• add documentation and tutorial example by @Leona-LYT in #23

Full Changelog: v0.1.0...v0.1.1