# H2O Generalized Linear Model Learner (Regression) - H2O Model Port to variable or table

**URL:** <https://forum.knime.com/t/h2o-generalized-linear-model-learner-regression-h2o-model-port-to-variable-or-table/66079>\
**Category:** KNIME Analytics Platform\
**Created:** [April 25, 2023, 3:19pm UTC](https://forum.knime.com/t/h2o-generalized-linear-model-learner-regression-h2o-model-port-to-variable-or-table/66079 "2023-04-25T15:19:53Z")\
**Posts on this page:** 1\
**Showing post:** 5

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**Author:** ![mlauber71](https://forum-cdn.knime.com/user_avatar/forum.knime.com/mlauber71/32/73590_2.png) [@mlauber71](https://forum.knime.com/u/mlauber71)\
**Post date:** [May 17, 2023, 10:18pm UTC](https://forum.knime.com/t/h2o-generalized-linear-model-learner-regression-h2o-model-port-to-variable-or-table/66079/5 "2023-05-17T22:18:55Z")

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@jichaohe @jichaohe @elliegreib @kevweath well actually there is a way to do this. One could employ a Python node and H2O.ai in the background to extract a buch of parameters and also the summary from the MOJO model file. Though admittedly not the most elegant way you could have the information in a file 🙂

 ![image](https://forum-cdn.knime.com/uploads/default/original/3X/4/9/493fd392328b0a69e2dde8ead151716ea77cc04d.jpeg)

> **[H2O.ai - extract the summary and parameters from a MOJO model file – mlauber71](https://hub.knime.com/mlauber71/spaces/Machine_Learning/latest/ml_regression/kn_example_ml_regression_h2o_glm_parameter~Dl4NK9LmihH7Tu31)**
>
> H2O.ai - extract the summary and parameters from a MOJO model file

[kn\_example\_ml\_regression\_h2o\_glm\_parameter.knwf](https://forum.knime.com/uploads/short-url/hqgHTMrxn7Zs7acIt22CqEFaGgW.knwf) (222.2 KB)

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If you are interested you might also check out these article and workflows (I plan on having a walkthrough on some H2O.ai AutoML code also …):

> **[KNIME, XGBoost and Optuna for Hyper Parameter Optimization](https://medium.com/@mlxl/knime-xgboost-and-optuna-for-hyper-parameter-optimization-dcf0efdc8ddf)**
>
> TL;DR: Machine Learning gets better with hyper parameter optimisation and a tool like Optuna is there to help. Also you can integrate the…

> **[Hyperparameter optimization for LightGBM — wrapped in KNIME nodes](https://medium.com/low-code-for-advanced-data-science/hyperparameter-optimization-for-lightgbm-wrapped-in-knime-nodes-ddb7ae1d7e2)**
>
> TL;DR: Let a KNIME node find the right hyperparameters for your LightGBM ML model. Also use vtreat and see if that improves the results

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