Fill in Variable Importance user guide page with info about non-tree-based models

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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For some reason our [Variable Importance page|http://docs.h2o.ai/h2o/latest-stable/h2o-docs/variable-importance.html] only contains information about tree-based models. We should copy the information about how varimp is calculated for all algorithms (from their individual user guide pages), so that this page can serve as a central point for all variable importance related information.

There should be a subsection for each algorithm type, which at the very least points to a varimp section on their own algorithm page (but probably better just to duplicate it here).

Note that Stacked Ensembles do not currently have variable importance available: [https://0xdata.atlassian.net/browse/PUBDEV-5137|https://0xdata.atlassian.net/browse/PUBDEV-5137|smart-link]

h2oai / h2o-3

Fill in Variable Importance user guide page with info about non-tree-based models #7819