OptBinning 0.3.0

New additions:

• Class OptBinning introduces a new constraint to reduce dominating bins, using parameter gamma.
• Metrics HHI, HHI regularized and Cramer's V added to binning_table.analysis method. Updated quality score.
• Added column min/max target and zeros count to ContinuousOptimalBinning binning table.
• Binning algorithms support univariate outlier detection methods.

Tutorials:

• Tutorial: optimal binning with binary target. New section: Reduction of dominating bins.
• Enhance binning process tutorials.

OptBinning 0.2.0

New additions:

• Binning process to support optimal binning of all variables in dataset.
• Add print_output option to binning_table.analysis method.
• New unit tests added.

Tutorials:

• Tutorial: Binning process with Scikit-learn pipelines.
• Tutorial: FICO Explainable Machine Learning Challenge using binning process.

Bugfixes:

• Fix OptBinning.information print level default option.
• Avoid numpy.digitize if no splits.
• Compute Gini in binning_table.build method.

OptBinning 0.1.1

Bugfixes:

• Fix a bug in OptimalBinning.fit_transform when calling tranform internally.
• Replace np.int by np.int64 in model_data.py functions to guarantee 64-bit integer on Windows.
• Fix a bug in _chech_metric_special_missing.

OptBinning 0.1.0

First release of OptBinning.