Error when importing KNIME's Model file in Jupyter

[mlauber71]

@JaeHwanChoi the model writer will zip a given model to store it. Underneath there has to be a model that the package that you used can read(KNIME ML to Python - #2 by mlauber71 ). If you want to use it with Jupyter notebooks this has to work in both environments.

Saving XGBOOst Model to PMML - possible now?

@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 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…

You could try and store a model just as pickel and use knime as interface for some python models. Here are examples from several versions of knime and python:

use Python and KNIME to make a random forest – mlauber71

Simple example to make a random forest model with new Python Scrip in KNIME...

Simple example to make a random forest model with new Python Scrip in KNIME...

There are other frameworks you could use. Knime started to generically support Sklearn for certain models:

Sklearn: The KNIME Nodes for Scikit-Learn (sklearn) Algorithms - used to...

If you want to interact between knime and python maybe the H2O.ai framework is for you. You can interchange models via MOJO files.

Saving XGBOOst Model to PMML - possible now?

@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 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…

You might derive further inspiration from this collection:

knime_meets_python/machine_learning/binary at main · ml-score/knime_meets_python