Feature Selection for Random Forest Classification

[mlauber71]

@Haroon_954 this article about automated machine learning also has a passage about variable importance, currently if you want to have the power of feature importance with H2O Automl you would have to use R or Python.

H2O.ai AutoML in KNIME for classification problems

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…

Also most H2O.ai model nodes have a generic feature importance output (like GBM)

KNIME H2O Machine Learning Integration

If you want more advanced explanations you could look at examples like this, using a special global feature importance component.

Explaining the Credit Scoring Model with Model Interpretability Components –...

If you go into Python there you could use the powerful XGBoost with feature importance (the generic KNIME integration does not have that):

XGBoost Feature Importance

Not possible with nodes but with python scripting. See: And official xgboost documentation: https://xgboost.readthedocs.io/en/latest/python/python_api.html?highlight=get_score#xgboost.Booster.get_score Note that it’s important to see that xgboost has different types of “feature importance”. The Solution: What is mentioned in the Stackoverflow reply, you could use SHAP to determine feature importance and that would actually be available in KNIME (I think it’s still in the KNIME Labs categ…