# H2O GBM Regression Top Vars

**URL:** <https://forum.knime.com/t/h2o-gbm-regression-top-vars/21425>\
**Category:** KNIME Extensions\
**Created:** [February 27, 2020, 10:48pm UTC](https://forum.knime.com/t/h2o-gbm-regression-top-vars/21425 "2020-02-27T22:48:36Z")\
**Posts on this page:** 1\
**Showing post:** 2

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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:** [February 28, 2020, 6:27am UTC](https://forum.knime.com/t/h2o-gbm-regression-top-vars/21425/2 "2020-02-28T06:27:26Z")

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You could just use the learner

> **[H2O Gradient Boosting Machine Learner](https://hub.knime.com/knime/extensions/org.knime.features.ext.h2o/latest/org.knime.ext.h2o.nodes.learner.gbm.classification.H2OGBMClassificationLearnerNodeFactory3)**
>
> Learns a Gradient Boosting Machine (GBM) classification model using H2O .

 ![image](https://forum-cdn.knime.com/uploads/default/original/2X/7/7ab0f37c38fdbeab2d819d69f84d97c209dbc8d7.jpeg)

Or you could use the H2O Automl wrapper and exclude all methods besides GBM 🙂

> [@H2O.ai AutoML in KNIME for classification problems](https://forum.knime.com/t/h2o-ai-automl-in-knime-for-classification-problems/20923):
>
> H2O.ai Automl - a powerful auto-machine-learning framework wrapped with KNIME It features various models like Random Forest or XGBoost along with Deep Learning. It has wrappers for R and Python but also could be used from KNIME. The results will be written to a folder and the models will be stored in MOJO format to be used in KNIME (as well as on a Big Data cluster via Sparkling Water). One major parameter to set is the running time the model has to test various models and do some hyperparam…

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