0.0.20

Enhancements

• Support one-vs-rest logistic regression models (#36)

0.0.19

Enhancements

• Updated automatic wheel building on CI to use GitHub Actions (#33)

0.0.18

Bugfixes

• Fixed a problem that could block logistic regression from working in new versions of scikit-learn (4b6e11c).
• The input array shape now gets validated, and pandas dataframes gets subscripted and reordered to match the PMML file (2555898).

Enhancements

• 🌟Added support for support vector machine classification and regression (#31).
• Several improvements to documentation and code style.

0.0.17

Bugfixes

• Fixed an issue with categorical features that occurred when categories contained spaces.

Enhancements

• 🌟Added support for tree-based regression, including decision trees (PMMLTreeRegressor), random forests (PMMLForestRegressor) and gradient boosting (PMMLGradientBoostingRegressor) (#25).
• Added support for classification with linear models, with PMMLLogisticRegression for regression models, and PMMLRidgeClassifier for general regression models (#24).

0.0.16

Bugfixes

• Fix compatibility issues with the latest version of scikit-learn
• prevent inheritance problem related to _more_tags property

Enhancements

• 🌟Support gradient boosting classifiers, including categorical features (#20)
• Add an example that highlights the usage of tree.decision_path

0.0.15

Bugfixes

• Correctly parse ensemble trees with only a subset of target features. This happened to be the case for certain PMML models created with MatLab (#21)

0.0.14.1

Bugfixes

• Prevent Buffer dtype mismatch on 64 bit windows (reported in #19)

0.0.14

Bugfixes

• LocalTransformations are now parsed rather than only the global TransformationDictionary

Enhancements

• Added support for multiSplit decision trees
• Support average multipleModelStrategy for Random Forests

0.0.13

Bugfixes

• Ensures compatibility with the latest scikit-learn versions.

0.0.12

Bugfixes

• Define a minimum numpy version to ensure the library is compatible. Fixes issue #18.