Integrate TE as CategoricalEncoding in MOJO

h2oai / h2o-3

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

http://h2o.ai

Apache License 2.0

6.92k stars 2k forks source link

Integrate TE as CategoricalEncoding in MOJO #7843

Open exalate-issue-sync[bot] opened 1 year ago

exalate-issue-sync[bot] commented 1 year ago

requirements:

TE may encode only some categorical columns, so that the Mojo should be able to apply a default categorical encoding after TE.
In some cases, AutoML will apply multiple TEs (each with a different config), so the Mojo should be able to apply multiple TE/CatEncoding.

h2o-ops commented 1 year ago

JIRA Issue Migration Info

Jira Issue: PUBDEV-7799 Assignee: Sebastien Poirier Reporter: Sebastien Poirier State: In Progress Fix Version: N/A Attachments: N/A Development PRs: Available

Linked PRs from JIRA

https://github.com/h2oai/h2o-3/pull/6055 https://github.com/h2oai/h2o-3/pull/6113 https://github.com/h2oai/h2o-3/pull/4958