Optbinning

New features:

- Outlier detector``YQuantileDetector`` for continuous target 203.

Improvements

- Add support to solver SCS and HIGHS for optimal piecewise binning classes.
- Unit testing outlier detector methods.

Bugfixes

- Pass ``lb`` and ``ub`` as keyword arguments to RoPWR fit method (required since ropwr>=0.4.0).

New features:

- Treatment of special codes separately for optimal piecewise binning classes 191.

Improvements

- Allow plot ``style="actual"`` for stochastic optimal binning.
- Unit testing optimal piecewise binning classes 93.
- Unit testing add macOS Monterey 12.

Bugfixes

- Fix sample weight for ``BinningProcess`` when ``n_jobs != 1`` 190.
- Fix transform method for optimal binning 2D when dtype is categorical 197.
- Fix ``max_pvalue`` default value in documentation 199.

New features:

- New parameter ``show_bin_labels`` for binning tables 180.

New features:

- Optimal binning 2D support to categorical variables for binary and continuous target.

Improvements

- Integer intercept if ``rounding=True`` 165.
- Parameter ``show_digits`` applies to scorecard table bin column 170.

Bugfixes

- Fix ``Scorecard.score`` method when there are special and missing bins 179.

- Fix x and y axis labels in ``OptimalBinning2D`` plots, x and y were interchanged.

Fix setup.py find_packages excluding tests/.

New features:

- Optimal binning 2D with continuous target.

Improvements:

- Set tdigest and pympler dependencies as optional. This change avoids accumulation-tree issues faced by several users. Remove dill dependency.
- New continuous binning objective function leading to improvements in regression metrics.

Bugfixes:

- Fix binning 2D minimum difference constraints.


Tutorials:

- Tutorial: optimal binning 2D with continuous target