Release version 5.5.0

• Added support for xgboost till version 1.7.6
• Added support for statsmodels till version 0.14.0
• Added support for scikit-learn till version 1.3.0

Release version 5.4.0

• Added a custom exporter to convert pipeline with XGBoost models to PMML.

Release version 5.3.0

• Added algorithmName="randomForest" attribute as part of MiningModel element for Random Forest models (PR - #56)
• Added the derived fields defined in the TransformationDictionary section of the MiningModel as MiningFields within each of the Segment models for Random Forest models (PR - #57)

Release version 5.2.0

• Added support for xgboost 1.x.x version (till 1.5.2) #53

Release version 5.1.0

Performance Improvement

• Improved performance of xgboost exporter to a greater extend (PR - #50)

Release version 5.0.1

Bug fix

• #39 - For LightGBM exporter
• Tree node split threshold is now converted from float64 to float32 for scikit-learn's tree based models.

Release version 5.0.0

Major Changes

• Removed customized elements from PMML schema. Now, Nyoka is completely Official PMML 4.4.1 schema compliant.
• Dropped support for Keras and Retinanet exporter as these two exporters were using customized DeepNetwork element.

CICD

• Usage of Travis-CI is discontinued. Instead of that Github Actions is used

Release version 4.4.0

Minor Update

• Added support for PMML schema 4.4.1

Release version 4.3.0

Minor update

• Added support for the latest version of scikit-learn (<=0.23.1)

Bug fix

• Removed Python version constraint from setup.py. Now user can install nyoka when python version is >= 3.6

Release version 4.2.1

Added exporter for TrendMiner's fingerprint

• This can be imported as from nyoka.custom.trendminer import FingerprintToPmml