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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.
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GLRM archetypes in feature space #15275

Closed exalate-issue-sync[bot] closed 1 year ago

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

For non-numeric training features, an additional minimization step must be done to project the archetypes back into the "closest" feature. This is equivalent to doing the reconstruction (prediction) with X consisting of indicator columns, i.e. [[1,0,0,...,0], [0,1,0,...,0], ..., [0,0,0,...,1]]

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

Former user commented: The R function is now available as h2o.proj_archetypes(myGLRMmodel, myGLRMtrain) with an optional reverse_transform = TRUE/FALSE parameter, indicating whether the final projection should be re-scaled (assuming we transformed the training data with say "DESCALE" when building the GLRM model). The Python function is basically identical, myGLRMmodel.proj_archetypes(myGLRMtrain).

DinukaH2O commented 1 year ago

JIRA Issue Migration Info

Jira Issue: PUBDEV-2368 Assignee: Former user Reporter: Former user State: Resolved Fix Version: N/A Attachments: N/A Development PRs: N/A