what kind of data can be used as a xgboost model?

[mlauber71]

One idea could be to use a H2O GBM model and take a look at the variable importance. That might give you and iead what is going on. If one varibale takes all the explaining power that could give you a hint.

Also you could try AutoML and see where this leads you

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…

If you use MacOSX or Linux you could exclude all model types besindes XGBoost and see waht that does, maybe to get some ideas about good parameters. But always be careful with the split of test and training and validation data.