# Saving XGBOOst Model to PMML - possible now? **URL:** \ **Category:** KNIME Analytics Platform\ **Created:** [July 13, 2022, 1:34pm UTC](https://forum.knime.com/t/saving-xgboost-model-to-pmml-possible-now/45057 "2022-07-13T13:34:40Z")\ **Posts on this page:** 1\ **Showing post:** 4
**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:** [July 13, 2022, 6:53pm UTC](https://forum.knime.com/t/saving-xgboost-model-to-pmml-possible-now/45057/4 "2022-07-13T18:53:59Z")
@knimebine007 I tried a few things but it seems not to be possible to transfer models of xgboost between knime and python. You could set up xgboost in python nodes. I will habe to see if I can make an example. Best way to transfer more complex models between knime, Python and R is the H2O.ai MOJO format > [@I can not use mojo reader node of H2O](https://forum.knime.com/t/i-can-not-use-mojo-reader-node-of-h2o/18878/7): > > @armingrudd from the description it sounds like @HaveF is using the free automl library \*1) from H2O that offers xgboost (on Mac and Linux as of now), some Deep Learning, stacked ensembles alongside Hyper Parameter optimization and an export of the resulting models as MOJO files - some of which can be reused in KNINE and even Sparkling Water environment, which is just great. But in order to be compatible with KNIME one would have to [exclude certain algorithms](http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/algo-params/exclude_algos.html) so the mojos would be reusable in K… > [@KNIME ML to Python](https://forum.knime.com/t/knime-ml-to-python/44220/2): > > @BrBr two options would be to use H2O.ai models or PMML Depending on you data. From my experience when a robust tree model is of any use often H2O.ai GBM does come close (To XGBoost). You could let that train with automl an benefit from the grid search within or set up you own hyper parameter search. --- _[View the full topic](https://forum.knime.com/t/saving-xgboost-model-to-pmml-possible-now/45057)._