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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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Create variable importance matrix/plots for the final stacked ensemble models within AutoML leaderboard #7513

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

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

User has suggested that they would like to have feature/variable importance matrix/plots available for the stacked ensemble models in the AutoML leaderboard.

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

Neema Mashayekhi commented: As a workaround, can use permutation feature importance [https://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/permutation-variable-importance.html|https://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/permutation-variable-importance.html]

h2o-ops commented 1 year ago

JIRA Issue Details

Jira Issue: PUBDEV-8137 Assignee: Tomas Fryda Reporter: Arun Aryasomayajula State: Open Fix Version: N/A Attachments: N/A Development PRs: N/A