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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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Exposure weighted coefficients for GLM models varimp #16345

Open genrichards opened 3 months ago

genrichards commented 3 months ago

Is your feature request related to a problem? Please describe. A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]

I noticed that the varimp() for h2o.glm is just the sum of the absolute values of standardised coefficients. Have you ever considered using the exposure weighted coefficients (eg a 5% increase on 50% of the book is more importance than a 100% increase on 1% of the book) Describe the solution you'd like A clear and concise description of what you want to happen. e.g. a 5% increase on 50% of the book is more importance than a 100% increase on 1% of the book. Describe alternatives you've considered A clear and concise description of any alternative solutions or features you've considered, if applicable.

Additional context Add any other context or screenshots about the feature request here. If there's a reference (paper, book, etc) for this feature, please add that here.

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wendycwong commented 2 months ago

@genrichards : Is there any reference on this method?