Openscoring
@openscoring
We bring predictive models to enterprise Java applications
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Size matters! Find out the true memory size of your #SkLearn models using the sklearn2pmml.util.deep_sizeof() utility func: openscoring.io/blog/2022/11/0…
openscoring.io
Measuring the memory consumption of Scikit-Learn models - Openscoring
Measuring the memory consumption of Scikit-Learn models - Openscoring
Extending #SkLearn with CHAID model type: openscoring.io/blog/2022/07/1…
openscoring.io
Extending Scikit-Learn with CHAID models - Openscoring
Extending Scikit-Learn with CHAID models - Openscoring
SkLearn2PMML 0.84(.2) added #SkLearn-style wrappers for the github.com/Rambatino/CHAID package. Let's grow some new tree species!
github.com
GitHub - Rambatino/CHAID: A python implementation of the common CHAID algorithm
A python implementation of the common CHAID algorithm - Rambatino/CHAID
JPMML-Transpiler has been upgraded from 1.2.X to 1.3.X! During modularization, the core module was renamed from 'org.jpmml:jpmml-transpiler' to 'org.jpmml:pmml-transpiler'.
SkLearn2PMML 0.83.0 approves this. All data structures unchanged, except for minor tweaks around GB and HistGB loss functions (again!)
scikit-learn 1.1 is out! What's new? You can check the release highlights there: bit.ly/3yFnT7Q pip install -U scikit-learn or conda install -c conda-forge scikit-learn #sklearn #ML #Datascience #opensource #Python
Extending #SkLearn with prediction post-processing: openscoring.io/blog/2022/05/0…
openscoring.io
Extending Scikit-Learn with prediction post-processing - Openscoring
Extending Scikit-Learn with prediction post-processing - Openscoring
JPMML-XGBoost now supports XGBoost 1.6.0 categorical splits and Universal Binary JSON (UBJ) model dumps. Included in SkLearn2PMML 0.82.0 and newer github.com/jpmml/sklearn2…
github.com
GitHub - jpmml/sklearn2pmml: Python library for converting Scikit-Learn pipelines to PMML
Python library for converting Scikit-Learn pipelines to PMML - jpmml/sklearn2pmml
New project for exporting fitted #Spark pipeline models to #PMML documents. Interoperable with #PySpark: github.com/jpmml/jpmml-sp…
Using #Spark pipeline models for real-time prediction: the #REST web service approach openscoring.io/blog/2016/07/0…
Interpreting or documenting #SkLearn models? Export to text using our next-gen #PMML converter: github.com/jpmml/sklearn2…
github.com
GitHub - jpmml/sklearn2pmml: Python library for converting Scikit-Learn pipelines to PMML
Python library for converting Scikit-Learn pipelines to PMML - jpmml/sklearn2pmml
@bdy11n Export using JPMML-XGBoost and interpret like any other gradient boosting model - tree paths, vars contributing to each path
Path to Personal Models? Deploy #Rstats and #Scikit-Learn models on #Android devices: github.com/jpmml/jpmml-an…
github.com
GitHub - jpmml/jpmml-android: PMML evaluator library for the Android operating system (http://www...
PMML evaluator library for the Android operating system (http://www.android.com/) - jpmml/jpmml-android
Converting XGBoost models to standardized PMML representation for easy analysis and deployment on Hadoop & Spark: github.com/jpmml/jpmml-xg…
github.com
GitHub - jpmml/jpmml-xgboost: Java library and command-line application for converting XGBoost...
Java library and command-line application for converting XGBoost models to PMML - jpmml/jpmml-xgboost
Architecting a Machine Learning System for Risk twistopayments.github.io/machine-learni… Thanks @airbnbnerds for the inspiration! #MachineLearning
.@clarler Several users in Netflix. Check out this C6Stephens' mod for multiple-model evaluation use case: github.com/c6stephens/ope…
Some more scikit-learn to PMML converters are coming out: github.com/jpmml/jpmml-sk… and a thin python wrapper github.com/jpmml/sklearn2…
github.com
GitHub - jpmml/sklearn2pmml: Python library for converting Scikit-Learn pipelines to PMML
Python library for converting Scikit-Learn pipelines to PMML - jpmml/sklearn2pmml
@openscoring great work releasing a pmml extractor for #scikit-learn! #python #datascience
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