h2o-pysparkling-3.0 3.46.0.1.post1

Sparkling Water integrates H2O's Fast Scalable Machine Learning with Spark

Stars: 953, Watchers: 953, Forks: 362, Open Issues: 45

The h2oai/sparkling-water repo was created 9 years ago and the last code push was Yesterday.
The project is popular with 953 github stars!

How to Install h2o-pysparkling-3-0

You can install h2o-pysparkling-3-0 using pip

pip install h2o-pysparkling-3-0

or add it to a project with poetry

poetry add h2o-pysparkling-3-0

Package Details

Author

H2O.ai

License

Apache v2

Homepage

https://github.com/h2oai/sparkling-water

PyPi:

https://pypi.org/project/h2o-pysparkling-3.0/

GitHub Repo:

https://github.com/h2oai/sparkling-water

Classifiers

• Software Development/Build Tools

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Errors

A list of common h2o-pysparkling-3-0 errors.

Code Examples

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GitHub Issues

The h2o-pysparkling-3-0 package has 45 open issues on GitHub

• [SW-2684] Make io.fabric8.kubernetes-client just a complileOnly dependency to minimize size of uber jar
• [SW-2680] Expose SHAP values for H2OMOJOPipeline
• [SW-2681] Add Comment to Documentation about Contributions Support only in Binomial and Regression Models
• [SW-2651] Add info about overriding mojo2 library
• Missing provider for modeling steps XGBoost when training AutoML
• [SW-2674] ChicagoCrimeApp refactor
• [SW-2646] Calculate Metrics on Arbitrary Dataset
• [SW-2622] Avoid Multiple Materialization of Spark DataFrame During Conversion to H2OFrame

See more issues on GitHub